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GCRI · علم سامانه‌ها و مدل‌سازی

آمار برای مدل‌سازی

Probability and statistics are the language of uncertainty in modelling. In this field they underpin calibration, inference about parameters, evaluation of model performance and the honest reporting of ranges, for every modelling platform in the network.

01ثبت‌خانه

Statistics for modelling on the record.

Method recordSTEM method
  1. روشاحتمال و آمار
  2. متولیسامانه‌ها Science and Modelling
  3. Applied inعلوم سامانه‌ها و مدل‌سازی
  4. داده3 inputs
  5. Canonical pageSTEM Commons
02Application

What probability and statistics does here.

Probability and statistics provide the mathematical framework for describing uncertainty and learning from data. They underpin estimation, testing and prediction in every risk discipline. In methods and modelling, risk management and ریسک سامانه‌ای and research and evidence, statistical methods quantify the likelihood and uncertainty of losses across interconnected سامانه‌ها. They make the evidence behind risk decisions explicit and reproducible. The canonical description is on STEM Commons.

Bayesian calibration

Model parameters are calibrated with Bayesian methods that combine prior knowledge with data and produce posterior distributions, including a term for model discrepancy.

Proper scoring of predictions

Probabilistic model outputs are scored with proper scoring rules so that honest uncertainty is rewarded.

Ensemble statistics

Ensembles of model runs are summarised with statistical methods that account for dependence between ensemble members.

03روش‌ها و شواهد

How statistics for modelling is modelled, measured and tested.

مدل، مفروضات، داده‌ها و عدم قطعیت آن، به شکلی که دانشمند یا مهندس بررسی می‌کند. ساختارها از روش‌های منتشرشده پیروی می‌کنند. مقادیر ترسیم‌شده تبیینی‌اند، نه یافته.

مدل، معادلات و مفروضات

The model structure used for statistics for modelling, the assumptions it makes and how each is tested.

ریسک عنصر iRi = f(Hi, Ei, Vi, Ci)مخاطره، مواجهه، آسیب‌پذیری و ظرفیت مقابله
زیان سالانهٔ مورد انتظارEAL = ∑k pk · Lkبر رویدادهای k با احتمال سالانهٔ pk
سامانه‌های جفت‌شدهxt+1 = A xt + B ut + εtA carries the couplings, u the shock, ε the error
فرضچرا ساخته می‌شودچگونه آزموده می‌شود
مخاطره و مواجهه در یک رویداد مستقل‌اندانتگرال زیان را تحت‌کنترل نگه می‌داردمقایسه با برازش مفصل بر سوابق مشترک
جفت‌شدگی‌ها نزدیک نقطهٔ کار خطی‌انداجازه می‌دهد شوک‌ها از طریق A منتشر شوندفراتر از دامنهٔ خطی آشفته کنید و مقایسه کنید
منحنی‌های آسیب‌پذیری میان مکان‌ها منتقل می‌شوندمنحنی‌های محلی به‌ندرت در دسترس‌انداعتبارسنجی در برابر زیان‌های مشاهده‌شده در جاهایی که وجود دارند
ورودی‌ها
مجموعه‌داده‌های نسخه‌دار با خاستگاه
خروجی‌ها
توزیع‌ها، هرگز تک‌رقم‌ها
کد
منتشرشده با کارت مدل و آزمون‌ها
بازتولید
همان داده، کد، محیط و بذر
فرمول‌بندی‌های استاندارد ریسک و فضای حالت؛ مفروضات فهرست‌شده نمونهٔ روش‌اند، برای داوری بیان شده‌اند و نتیجه نیستند.
اصول راهنمای FAIR · GO FAIR و FORCE11W3C PROV · کنسرسیوم وب جهانیGuidance on consistent treatment of uncertainties · IPCCCEOS Analysis Ready Data · Committee on Earth Observation Satellites
04داده

What the method consumes.

Datasets and records used by probability and statistics on this platform, with the role each plays and the label its values carry. Sources are listed in the source register.

دادهنقشبرچسب
Observational datasetsCalibration and evaluationمشاهده‌شده
Model ensemble outputsآمارمدل‌سازی‌شده
Prior information from literatureBayesian priorsگزارش‌شده
05فرایند

نحوهٔ اجرای فرایند.

Process explorer · Process

Statistics for modelling: Catastrophe model pipeline

10 steps5 مرحله

فرایند

Statistics for modelling: Catastrophe model pipeline

Catastrophe models usually have hazard, vulnerability, exposure and financial modules, and produce loss tables, میانگین زیان سالانه, exceedance curves, PML and TVaR.

  1. 01 Hazard

    1. 01Event catalogueDataset · Model developerThe hazard module holds a large catalogue of simulated events that represents a wide range of plausible scenarios.
    2. 03Hazard moduleModel · Model developerFor each event the hazard module gives the local hazard intensity that the vulnerability module reads.
  2. 02 Exposure and damage

    1. 02مواجههDataset · Validation reviewerThe exposure module houses the portfolio data, such as location-specific information.
    2. 04Vulnerability moduleModel · Model developerThe vulnerability module quantifies the expected damage to the exposures from an event.
  3. 03 Financial

    1. 05Financial moduleModel · Validation reviewerThe financial module calculates losses from all event scenarios. Ground-up, gross and net losses differ as shown below.
    2. 06Event loss tableDataset · Validation reviewerAn event loss table lists each event with its annual occurrence probability and its loss for the portfolio.
    3. 07Year loss tableDataset · Validation reviewerSome models also sample simulated years from event losses. This outline does not describe their format.
  4. 04 Metrics

    1. 08احتمال فراگذشت curvesModel · Validation reviewerThe occurrence curve gives the chance that the largest loss in a year exceeds an amount. The aggregate curve does so for the sum of losses.
    2. 09AAL, PML and TVaRModel · Validation reviewerAverage annual loss sums event probability times loss. PML is one point on the occurrence curve.
  5. 05 Use of results

    1. 10Use of resultsDecision · Validation reviewerThe tail metrics are used when considering a portfolio’s exposure and the level of catastrophe reinsurance an insurer requires.

Outline of an external standard or method, from its public sources. It describes what the framework says, not a record of any run. It does not show that Nexus or any partner performs, certifies or approves a step. A gate is a step of the method, never a Nexus approval.

06اعتبارسنجی

How results are checked.

Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.

بررسیHow it is done
Posterior predictive checksCalibrated models must reproduce observed data
Prior sensitivityResults are recomputed under alternative priors
Scoring on held-out dataPredictions are scored on data not used in calibration

Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.

08تولیت

Who maintains the method.

سامانه‌ها Science and Modelling stewards probability and statistics for the Consortium and reviews how it is applied across platforms.

09پرسش‌ها

پرسش و پاسخ.

What are the limits of probability and statistics in this field?
Inference depends on assumptions about independence, distribution and sampling that may not hold. Small or biased samples limit reliability.
How are results checked?
Results are checked through diagnostics, cross-validation and sensitivity to assumptions. Independent replication confirms findings.
Are the values on this page real?
The data names, roles and checks are real descriptions of practice. The canvas shows a design of the method’s use, not a run; no result is reported here.
Where else is this method applied?
رصدخانهٔ canonical method page lists every platform that applies it.
گزارش‌هامشارکت

شواهد را از همین صفحه ارسال کنید.

گزارش‌ها is the Consortium’s quarterly publication. What you know about this subject can become evidence, a case or a correction in the next edition, credited to you and published under CC BY 4.0 where it is your own work.

GCRIعلوم سامانه‌ها و مدل‌سازیروش‌ها و فنیسناریو و آزمون تنش
علوم سامانه‌ها و مدل‌سازی

یک روش، یک مجموعه‌داده یا یک پرسش بیاورید.

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