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:where([role=\"tablist\"],[role=\"toolbar\"],[hidden],button,.nx-theme-toggle,.nx-live-slot){display:none}#nxe-n7jcghra.nxe img{max-width:100%;height:auto}<\/style><svg class=\"nx-defs\" aria-hidden=\"true\" focusable=\"false\" width=\"0\" height=\"0\"><defs><linearGradient id=\"nx-gi\" x1=\"0\" y1=\"0\" x2=\"1\" y2=\"1\"><stop offset=\"0\" class=\"nx-gi-a\"\/><stop offset=\"0.55\" class=\"nx-gi-b\"\/><stop offset=\"1\" class=\"nx-gi-c\"\/><\/linearGradient><\/defs><\/svg><section class=\"nx-hero nx-fh\" data-layout=\"faculty\" data-ground=\"none\" data-ah=\"discipline\" data-ah-len=\"long\" aria-labelledby=\"nx-ast-title\"><div class=\"nx-container\"><div class=\"nx-fh-grid\"><div class=\"nx-hero-text nx-ah-text\"><p class=\"nx-ah-kicker\"><span>Risk Academy, a Nexortium programme<\/span><span>Risk Science and Methods<\/span><span>Department of Quantitative Risk Analysis<\/span><\/p><p class=\"nx-ah-accent\"><svg class=\"nx-ah-motif\" data-motif=\"bands\" viewBox=\"0 0 44 16\" aria-hidden=\"true\" focusable=\"false\"><path data-k=\"b\" d=\"M1 5C12 3 30 2 43 1M1 11C12 13 30 14 43 15\"\/><path d=\"M1 8C12 8 30 8 43 8\"\/><\/svg><span>Rates, return periods, exceedance curves, Monte Carlo and the limits of matrices<\/span><\/p><h1 id=\"nx-ast-title\" class=\"nx-hero-title nx-ah-title\">Quantitative risk analysis and statistics<\/h1><p class=\"nx-tagline nx-ah-lede\">Quantitative risk analysis turns data into probabilities with their errors attached.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"#nx-sec-maps\">Open the twin builder <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"#nx-sec-courses\">See the courses<\/a><\/div><p class=\"nx-fh-links\"><a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/risk-science\/departments\/quantitative-risk\/\">Back to Department of Quantitative Risk Analysis<\/a><\/p><\/div><div class=\"nx-fh-con\"><p class=\"nx-fh-con-h\"><span class=\"nx-tw-path\"><span>risksacademy.com<\/span><span>risk-science<\/span><strong>discipline<\/strong><\/span><\/p><div class=\"nx-fh-con-b\"><figure class=\"nx-ra-sig\" data-motif=\"bands\" data-scope=\"discipline\"><svg class=\"nx-ra-sig-svg\" viewBox=\"0 0 480 300\" role=\"img\" aria-label=\"Quantitative risk analysis and statistics, drawn as uncertainty bands: 0 primary courses open, 4 in preparation, 2 approved for authoring and 4 also taught here, across 8 subtopics. Courses: RAT-205 L2 in preparation, RAT-206 L2 in preparation, RAT-207 L2 in preparation, RAT-210 L3 in preparation, RAT-250 L3 approved for authoring, RAT-257 L3 approved for authoring, RAF-107 L1 also taught here, RAT-216 L3 also taught here, RAT-223 L2 also taught here, RAT-226 L2 also taught here.\" focusable=\"false\" data-nx-z=\"lZPRboMwDEV\/xfI7aeIY00qlz31gH8GAqZUYmwBNbF8\/haZSu8KSPmHEuTeOr9l\/luMJqrYchhy7KenLZDgh1Dm+WAYSOXLKuDnsF7j6wrEGolTZI7MESbNllcWRzHGctpFnC0UeTXTFxmYa77FxQphyZI3wnSNtGcFBSdlVp48+x\/dzXbcNHgqz37gP\/3iYLFU2ZENBG2u0ykI2Nmjjbhww4avJ4\/Rey+5mfiKtsamyiSECV3HCWmVzaUGDBuPCSPz7\/GTQP8vJOG+6NS\/mybkNKebLA1HBLEC0Em27oHaVV4tc5DJnXp37qm3uHd5ahMrHXrkhiSD0OdqQwGf8lMYH+pRmTu9Bsb4zfn3lT9RNV+NBwivn5Zle0uv1NRnGSxLi8iaRr7Wf0XPGiDJRoIjaxYBkMiVRoGzjerRmF9ejlV1cj0z6psdf\"><title>Quantitative risk analysis and statistics, drawn as uncertainty bands: 0 primary courses open, 4 in preparation, 2 approved for authoring and 4 also taught here, across 8 subtopics. Courses: RAT-205 L2 in preparation, RAT-206 L2 in preparation, RAT-207 L2 in preparation, RAT-210 L3 in preparation, RAT-250 L3 approved for authoring, RAT-257 L3 approved for authoring, RAF-107 L1 also taught here, RAT-216 L3 also taught here, RAT-223 L2 also taught here, RAT-226 L2 also taught here.<\/title><\/svg><figcaption class=\"nx-ra-sig-cap\"><span><span class=\"nx-ra-sig-t\">Quantitative risk analysis and statistics at a glance.<\/span> Drawn as uncertainty bands.<\/span><ul class=\"nx-ra-key\"><li data-k=\"live\">Open, 0<\/li><li data-k=\"prep\">In preparation, 4<\/li><li data-k=\"appr\">Approved for authoring, 2<\/li><li data-k=\"also\">Also taught here, 4<\/li><li data-k=\"topic\">Subtopics, 8<\/li><\/ul><span class=\"nx-ra-sig-src\">Course codes from the curriculum; study levels L1 to L4.<\/span><\/figcaption><\/figure><\/div><a class=\"nx-fh-go\" href=\"#nx-sec-maps\"><span class=\"nx-fh-go-k\">Twin builder and simulator<\/span><span class=\"nx-fh-go-t\">The central Apennines: L'Aquila, Amatrice and Norcia<\/span><span class=\"nx-fh-go-m\">4 twin generators, 8 layers, 4 instruments<\/span><\/a><\/div><\/div><dl class=\"nx-ah-figs\" data-n=\"4\"><div><dt>courses open now<\/dt><dd>0<\/dd><\/div><div><dt>in preparation<\/dt><dd>4<\/dd><\/div><div><dt>approved for authoring<\/dt><dd>2<\/dd><\/div><div><dt>reference earthquakes, M6.2 to M6.6<\/dt><dd>3<\/dd><\/div><\/dl><p class=\"nx-ah-fignote\">Twin figures for the central Apennines: L'Aquila, Amatrice and Norcia, counted from its layers at their stated dates.<\/p><\/div><\/section><section id=\"nx-sec-maps\" class=\"nx-as\" data-as=\"maps\" aria-labelledby=\"nx-sec-maps-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-maps-h\" class=\"nx-as-title\">Twin builder and simulator.<\/h2><p class=\"nx-as-lede\">For Quantitative risk analysis and statistics: the faculty's twin of L'Aquila on the planet map, the globe and in 3D. Pick a generator, switch layers, run the instruments; every layer prints its provider and date.<\/p><\/div><div class=\"nx-as-body\"><div class=\"nx-ra-map nx-tw\" data-ra-map=\"risk-science\" data-ra-map-od=\"J\/suhpDmyEM6vt\/FeKzBtfv97U9AlgeKOZNVS1zHFVo=\" data-ra-map-state=\"idle\" data-ra-map-mode=\"learn\" data-ra-view=\"map\" data-tw-pip=\"twin\"><div class=\"nx-tw-head nx-ra-map-bar\" role=\"toolbar\" aria-label=\"Twin builder controls\"><p class=\"nx-tw-path\"><span>risk-science<\/span><span>l'aquila<\/span><strong>twin builder<\/strong><\/p><div class=\"nx-ra-tb-search\" data-tw-search=\"true\"><\/div><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"View\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-map\" aria-pressed=\"true\">Planet map<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-globe\" aria-pressed=\"false\">Globe<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"view-twin\" aria-pressed=\"false\">3D twin<\/button><\/span><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"Extent\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"anchor\" aria-pressed=\"true\" disabled>L'Aquila<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"world\" aria-pressed=\"false\" disabled>World<\/button><\/span><span class=\"nx-ra-map-seg\" role=\"group\" aria-label=\"Mode\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"learn\" aria-pressed=\"true\" disabled>Learn<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"expert\" aria-pressed=\"false\" disabled>Expert<\/button><\/span><span class=\"nx-tw-more\"><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"share\" disabled>Share and cite<\/button><button type=\"button\" class=\"nx-ra-map-btn\" data-ra-map-act=\"tools\" disabled>Expert tools<\/button><\/span><\/div><div class=\"nx-tw-gens nx-ra-tb-gens\" role=\"group\" aria-label=\"Twin generators\"><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"shake\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Field<\/span><span class=\"nx-ra-tb-gt\">Event shaking field<\/span><span class=\"nx-ra-tb-gp\">ShakeMap intensity of the 2009 and 2016 main shocks.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"sample\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Sampling<\/span><span class=\"nx-ra-tb-gt\">Hazard sampling field<\/span><span class=\"nx-ra-tb-gp\">A density field of the live USGS catalogue in view: where events sample the hazard this week.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"lead\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Rings<\/span><span class=\"nx-ra-tb-gt\">Warning lead-time rings<\/span><span class=\"nx-ra-tb-gp\">Lead time an alert leaves around each reference event.<\/span><\/button><button type=\"button\" class=\"nx-ra-tb-gen\" data-ra-gen=\"exp\" aria-pressed=\"false\"><span class=\"nx-ra-tb-gk\">Exposure<\/span><span class=\"nx-ra-tb-gt\">Exposure under the events<\/span><span class=\"nx-ra-tb-gp\">GPW population density under the reference events.<\/span><\/button><\/div><div class=\"nx-tw-body\"><div class=\"nx-ra-map-views\"><div class=\"nx-ra-map-stage\"><div class=\"nx-ra-map-idle\"><button type=\"button\" class=\"nx-btn\" data-variant=\"secondary\" data-ra-map-act=\"load\">Load the map<\/button><\/div><\/div><div class=\"nx-ra-map-twin\"><div class=\"nx-ah\" data-ah=\"twin\"><div class=\"nx-ast\" data-ast=\"twin\" data-ast-state=\"flat\" data-ast-faculty=\"risk-science\" data-ast-mode=\"metres\" data-ast-od=\"KwWwtf3193b19APMOHPUbXw4RChtn3Zh8FJe1r+skOI=\" data-ast-data=\"ocHQ7mLOaEtl4b+N+wMxxmuM4FHvHPjlBnmWBZzsKAk=\" data-ast-exag=\"1.5\"><div class=\"nx-ast-stage\" tabindex=\"0\" role=\"region\" aria-label=\"Digital twin of The central Apennines: L'Aquila, Amatrice and Norcia\" aria-describedby=\"nx-ast-twin-help\"><figure class=\"nx-ra-sig nx-ast-flat\" data-scope=\"twin\" id=\"nx-ast-fig\"><svg class=\"nx-ra-sig-svg nx-ast-svg\" viewBox=\"0 0 1200 1133\" role=\"img\" aria-label=\"The central Apennines: L'Aquila, Amatrice and Norcia: a flat plan of the faculty twin. Terrain 24 to 2,805 m; 3 reference earthquakes, M6.2 to M6.6; Earthquakes M4.5 and above, past week (live): live, loads in the twin; USGS ShakeMap intensity contours: live, loads in the twin. Contours every 500 metres from the elevation grid; reference earthquakes as marks.\" focusable=\"false\" 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\"><title>The central Apennines: L'Aquila, Amatrice and Norcia: a flat plan of the faculty twin. Terrain 24 to 2,805 m; 3 reference earthquakes, M6.2 to M6.6; Earthquakes M4.5 and above, past week (live): live, loads in the twin; USGS ShakeMap intensity contours: live, loads in the twin. Contours every 500 metres from the elevation grid; reference earthquakes as marks.<\/title><\/svg><figcaption class=\"nx-ra-sig-cap\"><span><span class=\"nx-ra-sig-t\">The central Apennines: L'Aquila, Amatrice and Norcia, Italy.<\/span> Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.<\/span><ul class=\"nx-ra-key nx-ast-key\"><li data-k=\"terrain\">Terrain 24 to 2,805 m<\/li><li data-k=\"event\">3 reference earthquakes, M6.2 to M6.6<\/li><li data-k=\"live-quakes-live\">Earthquakes M4.5 and above, past week (live): live, loads in the twin<\/li><li data-k=\"live-shakemap\">USGS ShakeMap intensity contours: live, loads in the twin<\/li><\/ul><span class=\"nx-ra-sig-src\">Credits and dates per layer are listed under the stage. Relief in the twin is exaggerated \u00d71.5 for legibility.<\/span><\/figcaption><\/figure><p class=\"nx-ast-help\" id=\"nx-ast-twin-help\">Drag to move, right drag or Shift with a drag to turn and tilt, Ctrl with the wheel to zoom. Keyboard: arrows move, Shift with arrows turn and tilt, + and - zoom, 0 resets, V steps through the views, Escape clears.<\/p><\/div><div class=\"nx-ast-bar nx-ah-hud\" role=\"toolbar\" aria-label=\"Twin controls\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"open\" data-variant=\"primary\">Open the 3D twin<\/button><span class=\"nx-ah-status\" data-live=\"true\"><span class=\"nx-ah-dot\" aria-hidden=\"true\"><\/span>2 live layers, checked 17 Oct 2026<\/span><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"reset\" disabled>Reset view<\/button><span class=\"nx-ast-group\" aria-label=\"View\" data-ast-views=\"true\" data-ast-default=\"a-instrument\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"a-instrument\" aria-pressed=\"true\" disabled>Drawing<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"b-daylight\" aria-pressed=\"false\" disabled>Photo<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"style\" data-ast-style=\"c-holograph\" aria-pressed=\"false\" disabled>Holograph<\/button><\/span><details class=\"nx-ah-menu\" data-k=\"places\"><summary><span>Places<\/span><span class=\"nx-ah-n\">4<\/span><\/summary><div class=\"nx-ah-pop\"><span class=\"nx-ast-group\" aria-label=\"Views\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"laquila\" disabled>L'Aquila 2009<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"amatrice\" disabled>Amatrice and Norcia 2016<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"gran-sasso\" disabled>Gran Sasso<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"view\" data-ast-view=\"whole\" disabled>The whole box<\/button><\/span><\/div><\/details><details class=\"nx-ah-menu\" data-k=\"layers\"><summary><span>Layers<\/span><span class=\"nx-ah-n\">4<\/span><\/summary><div class=\"nx-ah-pop\"><span class=\"nx-ast-group\" aria-label=\"Layers\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"overlay\" aria-pressed=\"false\" hidden disabled>Analysis overlay<\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"terrain\" data-ast-delivery=\"inline\" aria-pressed=\"true\" disabled><span class=\"nx-ah-sw\" data-f=\"terrain\" aria-hidden=\"true\"><\/span><span>Terrain<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"events\" data-ast-delivery=\"inline\" aria-pressed=\"true\" disabled><span class=\"nx-ah-sw\" data-f=\"points\" aria-hidden=\"true\"><\/span><span>Reference earthquakes<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"quakes-live\" data-ast-delivery=\"browser-direct\" aria-pressed=\"false\" disabled><span class=\"nx-ah-sw\" data-f=\"live\" aria-hidden=\"true\"><\/span><span>Earthquakes M4.5 and above<\/span><span class=\"nx-ah-tag\">Live<\/span><\/button><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"layer\" data-ast-layer=\"shakemap\" data-ast-delivery=\"browser-direct\" aria-pressed=\"false\" disabled><span class=\"nx-ah-sw\" data-f=\"live\" aria-hidden=\"true\"><\/span><span>USGS ShakeMap intensity contours<\/span><span class=\"nx-ah-tag\">Live<\/span><\/button><\/span><\/div><\/details><span class=\"nx-ast-group nx-ah-time\" aria-label=\"Time\"><button type=\"button\" class=\"nx-ast-btn\" data-ast-act=\"replay\" disabled>Replay the events<\/button><\/span><button type=\"button\" class=\"nx-ast-btn nx-ah-tomap\" data-ast-act=\"to-map\" aria-controls=\"nx-sec-maps\"><span class=\"nx-ah-pin\" aria-hidden=\"true\"><\/span>Open in the map<\/button><details class=\"nx-ast-cred nx-ah-info\"><summary aria-label=\"Credits and data dates, The central Apennines: L'Aquila, Amatrice and Norcia\"><span class=\"nx-ah-i\" aria-hidden=\"true\">i<\/span><span class=\"nx-ah-it\">Credits<\/span><\/summary><div class=\"nx-ah-pop\"><ul class=\"nx-ast-credits\"><li data-ast-layer=\"terrain\"><strong>Terrain<\/strong> Surface height in whole metres on the plan's grid of cells about 455 by 605 metres; the box rises from 24 m in the Tiber valley to 2,805 m on the Gran Sasso. <span class=\"nx-ast-cr\">Copernicus DEM GLO-30 (c) DLR e.V. 2010-2014 and (c) Airbus Defence and Space GmbH 2014-2018 provided under COPERNICUS by the European Union and ESA. read 2026-10-17.<\/span> <span class=\"nx-ast-est\">Estimate: A digital surface model: trees and roofs are in the heights. Relief exaggeration is printed on the stage.<\/span><\/li><li data-ast-layer=\"events\"><strong>Reference earthquakes<\/strong> The three reference main shocks (L'Aquila 2009, Amatrice 2016, Norcia 2016): origin, magnitude, depth, time and review status from the USGS detail record, the hypocentre drawn below the ground. <span class=\"nx-ast-cr\">U.S. Geological Survey Earthquake Hazards Program. read 2026-10-17.<\/span> <span class=\"nx-ast-est\">Estimate: Reported solutions as reviewed by the USGS; magnitudes and depths carry the USGS stated uncertainties, which the event page links rather than restates.<\/span><\/li><li data-ast-layer=\"quakes-live\"><strong>Earthquakes M4.5 and above, past week (live)<\/strong> Reviewed and automatic seismic solutions of the past week inside the box, with magnitude and depth, read from the world feed and filtered to the box in your browser; the distribution fitter reads the same world feed for its sample. <span class=\"nx-ast-cr\">U.S. Geological Survey; reported detections, automatic solutions preliminary; never a warning. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is read from metadata.generated and printed with it.<\/span> <span class=\"nx-ast-est\">Estimate: Reported detections; automatic solutions are preliminary; never a warning.<\/span><\/li><li data-ast-layer=\"shakemap\"><strong>USGS ShakeMap intensity contours<\/strong> The ShakeMap MMI contours of each reference event draped on the terrain, read from the cont_mmi.json file the event's detail record names: what one realisation of the sampler's field looked like. <span class=\"nx-ast-cr\">U.S. Geological Survey ShakeMap and U.S. Geological Survey PAGER (preliminary, automatically generated; see the USGS disclaimers), with the product version and time. Live: this page requests earthquake.usgs.gov when the layer is opened; the data date is read from properties.products.shakemap[0].updateTime and printed with it.<\/span> <span class=\"nx-ast-est\">Estimate: Preliminary and automatically generated; PAGER levels are USGS estimates with uncertainty, never a warning or a loss figure of the Academy.<\/span><\/li><\/ul><p class=\"nx-ast-why\">Why this place: One box of about 82 by 77 kilometres holds the 2009 L'Aquila (M6.3), 2016 Amatrice (M6.2) and Norcia (M6.6) earthquakes, the Gran Sasso massif and the normal-fault basins between them. It is the reference case for every method the faculty teaches: magnitude-frequency fitting on a dense catalogue, return periods, probabilistic hazard, model validation, and risk communication after the L'Aquila trial of 2012, which turned on how probabilities were communicated. Data quality: Copernicus DEM GLO-30 covers the relief in two tiles, the USGS detail records of the three main shocks are reviewed, and the live feed is open. It connects to the quantitative-risk-analysis, catastrophe-exposure-modelling, uncertainty-decision, risk-communication-literacy and geospatial-geoai disciplines and to the spatial intelligence pages of the ecosystem sites.<\/p><\/div><\/details><\/div><div class=\"nx-ast-read\" data-ast-out=\"read\" aria-live=\"polite\" hidden><\/div><p class=\"nx-ast-note\" aria-live=\"polite\">Flat plan of the same data: relief, the three reference epicentres with magnitude and depth, and the numbers below; the sampler's figure shows one precomputed field with its parameters. The 3D twin opens on devices with WebGL once its data and library have been checked.<\/p><\/div><\/div><\/div><button type=\"button\" class=\"nx-tw-swap\" data-ra-map-act=\"swap\" aria-label=\"Swap the main view and the inset\"><span class=\"nx-tw-swap-k\" data-tw-swap=\"true\">3D twin<\/span><span class=\"nx-tw-swap-v\">Swap<\/span><\/button><div class=\"nx-ra-tb-insp nx-tw-insp\" aria-live=\"polite\"><\/div><\/div><aside class=\"nx-tw-dock\" data-nx-tabs=\"dock\" aria-label=\"Simulator: instruments, layers and sources\"><div class=\"nx-tw-tabs\" role=\"tablist\" aria-label=\"Simulator panels\"><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-inst\" aria-controls=\"nx-tw-p-inst\" aria-selected=\"true\" tabindex=\"0\" data-key=\"inst\">Instruments<span>4<\/span><\/button><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-layers\" aria-controls=\"nx-tw-p-layers\" aria-selected=\"false\" tabindex=\"-1\" data-key=\"layers\">Layers<span>8<\/span><\/button><button type=\"button\" class=\"nx-tw-tab\" role=\"tab\" id=\"nx-tw-t-src\" aria-controls=\"nx-tw-p-src\" aria-selected=\"false\" tabindex=\"-1\" data-key=\"src\">Sources<\/button><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-inst\" aria-labelledby=\"nx-tw-t-inst\"><p class=\"nx-tw-empty\">The instruments run on the faculty home, on the same twin of the central Apennines: L'Aquila, Amatrice and Norcia:<\/p><ul class=\"nx-ai\"><li><a class=\"nx-ai-a\" href=\"https:\/\/risksacademy.com\/risk-science\/#nx-sec-instruments\"><span class=\"nx-ai-t\">Distribution fitter (magnitude-frequency)<\/span><span class=\"nx-ai-w\">The Gutenberg-Richter relation fitted by maximum likelihood to a sample of magnitudes, with the cumulative magnitude-frequency curve, the b-value and its standard error, and a completeness magnitude the learner moves to watch the fit move.<\/span><\/a><\/li><li><a class=\"nx-ai-a\" href=\"https:\/\/risksacademy.com\/risk-science\/#nx-sec-instruments\"><span class=\"nx-ai-t\">Return-period calculator<\/span><span class=\"nx-ai-w\">The annual rate of events at or above a threshold magnitude from the Gutenberg-Richter relation, the return period and the probability of at least one event in an exposure time under a Poisson process, with the rate, the b-value, the threshold and the time on sliders.<\/span><\/a><\/li><li><a class=\"nx-ai-a\" href=\"https:\/\/risksacademy.com\/risk-science\/#nx-sec-instruments\"><span class=\"nx-ai-t\">Monte Carlo sampler (the uncertainty field)<\/span><span class=\"nx-ai-w\">Synthetic earthquakes drawn from the magnitude-frequency model over the box, the intensity at every hexagonal cell from a published prediction equation with its sigma, and the probability per cell that an intensity threshold is reached in the exposure time, drawn on the terrain with its sampling error.<\/span><\/a><\/li><li><a class=\"nx-ai-a\" href=\"https:\/\/risksacademy.com\/risk-science\/#nx-sec-instruments\"><span class=\"nx-ai-t\">Scenario tree<\/span><span class=\"nx-ai-w\">A decision tree with a decision node (act now or wait) and a chance node (the event comes or not), solved by backward induction for the expected value of each branch and the value of perfect information, with the probability and the consequence values on sliders.<\/span><\/a><\/li><\/ul><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-layers\" aria-labelledby=\"nx-tw-t-layers\" hidden><div class=\"nx-tw-layers\" data-tw-layers=\"true\"><p class=\"nx-tw-empty\">8 layers switch here once the map runs.<\/p><\/div><\/div><div class=\"nx-tw-pane\" role=\"tabpanel\" id=\"nx-tw-p-src\" aria-labelledby=\"nx-tw-t-src\" hidden><p class=\"nx-tw-empty\">6 live and observed layers and 2 from the twin. Data: Natural Earth, USGS, GDACS, CIESIN, CIESIN\/Columbia, VIIRS, NASA EONET, U.S. Geological Survey Earthquake Hazards Program, U.S. Geological Survey ShakeMap and U.S. Geological Survey P. Each layer's provider, licence and date are listed here once the map runs.<\/p><div class=\"nx-tw-cred\" data-tw-cred=\"true\"><\/div><\/div><\/aside><\/div><p class=\"nx-ra-map-note\" role=\"status\">The map loads from this site as it nears the view (MapLibre 6.9.1, checked by hash).<\/p><\/div><\/div><\/div><\/section><section id=\"nx-sec-brief-answer\" class=\"nx-as\" data-as=\"intro\" aria-labelledby=\"nx-sec-brief-answer-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-brief-answer-h\" class=\"nx-as-title\">The discipline in brief.<\/h2><\/div><div class=\"nx-as-body\"><div class=\"nx-as-intro\"><p class=\"nx-as-stand\">The discipline teaches probability and return periods after Cornell (1968), loss metrics such as average annual loss and exceedance probability curves, Monte Carlo simulation with its sampling error, dependence and correlation, extreme value analysis, Bayesian updating and the programming tools that apply them, on the Apennine twin's fitter, calculator and sampler, together with the limits of qualitative matrices. A graduate can fit a distribution with its standard error, build an exceedance curve by sampling and say what a risk matrix cannot tell a decision maker.<\/p><div class=\"nx-as-side\"><p class=\"nx-as-brief\">This area covers the quantitative core of risk analysis: probability and return periods, loss metrics such as average annual loss and exceedance probability curves, Monte Carlo simulation, dependence, extreme value analysis, Bayesian updating and the programming tools used to apply them, together with the limits of qualitative matrices. It is taught in the department of Quantitative Risk Analysis, Faculty of Risk Science and Methods, through 4 courses with it as primary discipline and 4 more that also teach it.<\/p><\/div><\/div><\/div><\/div><\/section><section id=\"nx-sec-scope\" class=\"nx-as\" data-as=\"wide\" aria-labelledby=\"nx-sec-scope-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-scope-h\" class=\"nx-as-title\">What the discipline covers.<\/h2><p class=\"nx-as-lede\">8 subtopics, taught across its courses and labs.<\/p><\/div><div class=\"nx-as-body\"><dl class=\"nx-as-std\" data-cols=\"2\"><div><dt>Faculty<\/dt><dd><a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/risk-science\/\">Faculty of Risk Science and Methods<\/a><\/dd><\/div><div><dt>Department<\/dt><dd><a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/risk-science\/departments\/quantitative-risk\/\">Department of Quantitative Risk Analysis<\/a><\/dd><\/div><div><dt>Courses<\/dt><dd>0 open, 4 in preparation<\/dd><\/div><div><dt>Levels<\/dt><dd>L2 Intermediate, L3 Advanced<\/dd><\/div><div><dt>Labs<\/dt><dd>Exposure lab, Trigger lab<\/dd><\/div><\/dl><ul class=\"nx-list\"><li>Probability and return periods<\/li><li>Loss metrics and exceedance curves<\/li><li>Monte Carlo simulation<\/li><li>Dependence and correlation<\/li><li>Extreme value analysis<\/li><li>Bayesian updating<\/li><li>Risk analytics programming<\/li><li>Qualitative tools and their limits<\/li><\/ul><p class=\"nx-note nx-mt-3\">Not yet taught by a course: Bayesian methods for risk; Compound-event statistics.<\/p><\/div><\/div><\/section><section id=\"nx-sec-frontier\" class=\"nx-as\" data-as=\"timeline\" aria-labelledby=\"nx-sec-frontier-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-frontier-h\" class=\"nx-as-title\">What is changing now.<\/h2><p class=\"nx-as-lede\">Recent developments in quantitative risk analysis and statistics, each with its source; sources checked on 16 October 2026. Summaries are short readings of the source; follow the link for the full account.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-hub\" class=\"nx-as\" data-as=\"hub\" aria-labelledby=\"nx-sec-hub-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-hub-h\" class=\"nx-as-title\">The faculty hub.<\/h2><p class=\"nx-as-lede\">News and research as they are published, per discipline, with the faculty's other tools.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><div class=\"nx-ra-feeds\" data-ra-feeds=\"risk-science\" data-ra-feeds-od=\"xP0gAoHq0YAjPaEcahaMb6bZNrwoOzfk9sjm\/PIVCDw=\" data-ra-feeds-scope=\"quantitative-risk-analysis\"><p class=\"nx-tw-empty\">The newest research, preprints, news and humanitarian reports for Quantitative risk analysis and statistics, each with its source and date, load here. <a class=\"nx-link\" href=\"https:\/\/risksacademy.com\/risk-science\/research-hub\/\">Open the research and news hub<\/a><\/p><\/div><\/figure><\/div><\/div><\/section><section id=\"nx-sec-profile\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-profile-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-profile-h\" class=\"nx-as-title\">The methods it teaches.<\/h2><p class=\"nx-as-lede\">From the Academy's risk taxonomy: the discipline's own entries and those of its primary courses, with how many courses teach each.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"rZJNTsNADIWvYmVNFQFdptkgBCyQoIgDuDMusZiMI9uJWk6PGmgBUX5E2cxiZH9+z35V5AFCQrNZkVcTxUmnsizqau\/\/JBQQ0XGSCOOscO2pqKvm9F1lMy3qa\/JGogHmCJ1icA5Ulc1pXUmqq8R1ZR3m+kZlgQtO7OsjUPJeM3SkvG1NYgYtuXKwqhxbxsY9unJRTyFIr0a70nIzaDfsfOVKLcGAqSfAjGlt\/AvsyffYa8lOcIaaBIzbPqGz5FF+pI5ypLzxfuCUOdvji2bnYNCpPCi2LeeHg9H3OZA6cvb1qDpIXvL\/qD5H9QZkYaTD21ouru5+Jh+\/kveDb3tM7Og8EOi4GjMyayk7uEj6xVktSPeRXm6yWUYevs7+nqhf4hNqhCW2nHiziE8p3wavIfRDjc95IOVMsEwi8Y+0d0bH9xk=\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-courses\" class=\"nx-as\" data-as=\"courses\" aria-labelledby=\"nx-sec-courses-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-courses-h\" class=\"nx-as-title\">4 in preparation.<\/h2><p class=\"nx-as-lede\">No course with this primary discipline is open yet; these are planned, so you can prepare with the courses also taught here.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"3ZNNSwMxEIb\/ypCTQpet9euyDRTxICjUtXgfk6E7mE1CMq3tv5dtq1a0UKigeMt88M47D5PK8hyMw5yHyi8KY5Su4qdE0Shd5Yh+KysvRURZFSQFP9VXYZYyZXhhaUAazhATt5iWYDkbjo49VeWmuSo7ubWo7kOI5HtwBuwhJoqYUDj4t6Yy6iq4rdkJi0RTpSvHurI8\/2IuYWGCJaXr0aQY9M+3x33RKZ7UroooXXN+hpFHtxQ2m+3GS2m27O3SzoJCCizK5j1U3XZK33y75vcWWqUnZBrPBl0Pbgdw44VSS5ZRqAcnA2g68HBEWbhFoeN3XysyZcdoT04Xh3J6EBTOH6Dqv8Go\/4OMLg9gdD9DL9wxmhOsgN0FS86xn\/67YzrpHwDqeiGJWoJHdDNa\/77MGdBbqElmycOYEgebfwXbKYzsHL0hu+9tlcFt4lc=\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-labs\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-labs-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-labs-h\" class=\"nx-as-title\">How the subject is practised in labs.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><div class=\"nx-table-wrap\"><table class=\"nx-table\" data-stack=\"true\"><thead><tr><th scope=\"col\">Lab<\/th><th scope=\"col\">What you do in it<\/th><th scope=\"col\">Courses using it<\/th><\/tr><\/thead><tbody><tr><td data-label=\"Lab\">Exposure lab<\/td><td data-label=\"What you do in it\">Overlay people and assets with hazard footprints<\/td><td data-label=\"Courses using it\">2<\/td><\/tr><tr><td data-label=\"Lab\">Trigger lab<\/td><td data-label=\"What you do in it\">Set forecast thresholds, pre-agreed actions and messages<\/td><td data-label=\"Courses using it\">1<\/td><\/tr><\/tbody><\/table><\/div><\/figure><\/div><\/div><\/section><section id=\"nx-sec-also\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-also-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-also-h\" class=\"nx-as-title\">Courses from other disciplines.<\/h2><p class=\"nx-as-lede\">These courses count in their own primary discipline and also teach this one; they do not count towards its achievement as primary courses.<\/p><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-approved\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-approved-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-approved-h\" class=\"nx-as-title\">2 courses approved for authoring.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><div class=\"nx-cc\"><p class=\"nx-cc-h\"><span class=\"nx-tw-path\"><strong>Approved for authoring<\/strong><\/span><span>no enrolment and no achievement until authored and reviewed<\/span><\/p><ol class=\"nx-ra-reg\"><li><div><span class=\"nx-ra-code\">RAT-250<\/span><span class=\"nx-ra-reg-b\"><span class=\"nx-ra-reg-t\">Bayesian Methods for Risk<\/span><\/span><span class=\"nx-ra-state\" data-state=\"approved\">Approved for authoring<\/span><span class=\"nx-ra-reg-m\">L3 Advanced, about 10 hours (estimate)<\/span><\/div><\/li><li><div><span class=\"nx-ra-code\">RAT-257<\/span><span class=\"nx-ra-reg-b\"><span class=\"nx-ra-reg-t\">Compound-event Statistics<\/span><\/span><span class=\"nx-ra-state\" data-state=\"approved\">Approved for authoring<\/span><span class=\"nx-ra-reg-m\">L3 Advanced, about 10 hours (estimate)<\/span><\/div><\/li><\/ol><\/div><\/figure><\/div><\/div><\/section><section id=\"nx-sec-ladder\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-ladder-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-ladder-h\" class=\"nx-as-title\">What you can earn here.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-pathways\" class=\"nx-as\" data-as=\"route\" aria-labelledby=\"nx-sec-pathways-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-pathways-h\" class=\"nx-as-title\">Pathways that teach it.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"vZTLTgMxDEV\/xco+Da1YoWkkQEhsYNE\/sDKejtU8RrEL9O9Rn1CJFhCo61xf5zjXaVp+gRBRZGrym8VqoEVFK4pVpyaXTMY3JR5p7CuuxPgmsm\/w+AQN9JW6qelVB7lxrrIsBAO2lFajUJIbUPt1\/ebEYsa4EnXGNzJgPjZbGD8rkWBX0ri15CuhGj9jWcDO7bQwGX8NpQNWgfEYQllWIQGsBIrLea\/QU6XR3sChb9ya8m+kuSbbRZxLz8NJ0tttJeyFZ2mf6W0p9g6FWtiQP2HGOSXKejD4fmzJ+PF+GpOrS02DarJpc93692d\/yEp1qCwE646wMz7LPDkkYHIpZszKgQfUUlcWg3LJVgYKjJH\/I\/2Py4SZFStjhs\/NYNsMPpr9LA6XW44QOaGS3XwHpes4\/Ecu7reu21DsXH9I\/otFcCX6xrX84t8B\"><\/figure><\/div><\/div><\/section><section id=\"nx-sec-practice\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-practice-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-practice-h\" class=\"nx-as-title\">Where the subject is worked on.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><ul class=\"nx-ra-cite\"><li><span class=\"nx-ra-cite-o\">The Global Centre for Risk and Innovation (GCRI)<\/span><span><a class=\"nx-link\" href=\"https:\/\/therisk.global\/risk-management\/\">Systemic Risk Management<\/a><span class=\"nx-ra-cite-n\">Quantitative and qualitative methods<\/span><\/span><\/li><li><span class=\"nx-ra-cite-o\"><\/span><span><a class=\"nx-link\" href=\"https:\/\/therisk.global\/fields\/methods\/\">Methods and modelling<\/a><\/span><\/li><\/ul><p class=\"nx-ra-cite-f\">Cited as places where the subject is practised; the institutions named are sources here, and Risk Academy hosts every course.<\/p><\/figure><\/div><\/div><\/section><section id=\"nx-sec-foundry\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-foundry-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-foundry-h\" class=\"nx-as-title\">The Foundry system where it is practised.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\"><ul class=\"nx-ra-cite\"><li><span class=\"nx-ra-cite-o\">Nexus Foundry<\/span><span><a class=\"nx-link\" href=\"https:\/\/therisk.global\/nexus-foundry\/systems\/grix\/\">GRIx system<\/a><span class=\"nx-ra-cite-n\">Statistical risk products<\/span><span class=\"nx-ra-cite-n\">Data room, Reports room<\/span><\/span><\/li><\/ul><p class=\"nx-ra-cite-f\">Nexus Foundry system pages describe what each system and its rooms are designed to do; they are not a service on offer, and studying here gives no access to them.<\/p><\/figure><\/div><\/div><\/section><section id=\"nx-sec-terms\" class=\"nx-as\" data-as=\"plain\" aria-labelledby=\"nx-sec-terms-h\"><div class=\"nx-container\"><div class=\"nx-as-head\"><h2 id=\"nx-sec-terms-h\" class=\"nx-as-title\">Terms used on this page.<\/h2><\/div><div class=\"nx-as-body\"><figure class=\"nx-as-z\" data-nx-z=\"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\"><\/figure><\/div><\/div><\/section><section class=\"nx-as\" data-as=\"related\" aria-labelledby=\"nx-as-rel\"><div class=\"nx-container\"><h2 id=\"nx-as-rel\" class=\"nx-as-title nx-as-small\">Around Quantitative risk analysis and statistics.<\/h2><nav class=\"nx-related nx-mt-3\" aria-label=\"Related\"><a href=\"https:\/\/risksacademy.com\/risk-science\/catastrophe-exposure-modelling\/\">Catastrophe, exposure and vulnerability modelling <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/indicators-indices\/\">Risk indicators and composite indices <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/probabilistic-forecasting-verification\/\">Probabilistic forecasting and verification <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/ai-machine-learning-risk\/\">AI and machine learning for risk analysis <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/departments\/quantitative-risk\/\">Department of Quantitative Risk Analysis <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a href=\"https:\/\/risksacademy.com\/risk-science\/\">Faculty of Risk Science and Methods <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><\/nav><\/div><\/section><section class=\"nx-closing\" aria-labelledby=\"nx-close-1\"><div class=\"nx-container\"><span class=\"nx-eyebrow\">Quantitative risk analysis and statistics<\/span><h2 id=\"nx-close-1\" class=\"nx-section-title\">Prepare with a related course.<\/h2><p class=\"nx-lede\">Open courses in connected disciplines teach the methods this discipline will build on.<\/p><div class=\"nx-actions\"><a class=\"nx-btn\" data-variant=\"primary\" href=\"https:\/\/risksacademy.com\/courses\/\">Browse the course catalogue <svg class=\"nx-arrow\" viewBox=\"0 0 16 16\" aria-hidden=\"true\"><path d=\"M3 8h10M9 4l4 4-4 4\"\/><\/svg><\/a><a class=\"nx-btn\" data-variant=\"secondary\" href=\"https:\/\/risksacademy.com\/risk-science\/departments\/quantitative-risk\/\">Back to Department of Quantitative Risk Analysis<\/a><\/div><\/div><\/section><\/div><\/div><script class=\"_iub_cs_skip\">window.__nxMount ? window.__nxMount(document.currentScript) : null;<\/script>\n\n\n<script type=\"application\/ld+json\">{\"@context\":\"https:\/\/schema.org\",\"@graph\":[{\"@type\":\"DefinedTermSet\",\"@id\":\"https:\/\/risksacademy.com\/risk-science\/quantitative-risk-analysis\/#terms\",\"name\":\"Key terms: Quantitative risk analysis and statistics\",\"inLanguage\":\"en\",\"isPartOf\":{\"@id\":\"https:\/\/risksacademy.com\/risk-science\/quantitative-risk-analysis\/#webpage\"},\"hasDefinedTerm\":[{\"@type\":\"DefinedTerm\",\"name\":\"Exceedance probability\",\"description\":\"The probability that a loss or hazard exceeds a given level in a 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statistics&#8221;<\/span><\/a><\/p>\n","protected":false},"author":1,"featured_media":0,"parent":0,"menu_order":172,"comment_status":"closed","ping_status":"closed","template":"elementor_header_footer","meta":{"_buddyx_sub_header_visibility":"","_buddyx_sub_header_title_visibility":"","_hide_show_side_panel":"","_buddyxpro_page_sidebar":"","_buddyxpro_page_disable_header":"","_buddyxpro_page_disable_footer":"","_buddyxpro_page_content_width":"","_buddyxpro_page_header_style":"","_buddyxpro_page_color_mode":"","_buddyxpro_page_loader":"","footnotes":""},"class_list":["post-70","page","type-page","status-publish","hentry"],"_links":{"self":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages\/70","targetHints":{"allow":["GET"]}}],"collection":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages"}],"about":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/types\/page"}],"author":[{"embeddable":true,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/users\/1"}],"replies":[{"embeddable":true,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/comments?post=70"}],"version-history":[{"count":0,"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/pages\/70\/revisions"}],"wp:attachment":[{"href":"https:\/\/risksacademy.com\/risk-science\/wp-json\/wp\/v2\/media?parent=70"}],"curies":[{"name":"wp","href":"https:\/\/api.w.org\/{rel}","templated":true}]}}