38 capabilities
26 of 38 have a bound kernel or engine. States are shown as the registry records them; a request fails closed wherever an adapter, specialist model or governed data is still missing.
| 手法 | Capability ID | 状況 | 実行基盤 |
|---|---|---|---|
| Robust Distribution | risk.capability.universal-mathematics-statistics.robust.distribution.v1 |
Reference implementation | Julia · frontier_risk_math.robust_distribution |
| Empirical Distribution | risk.capability.universal-mathematics-statistics.empirical.distribution.v1 |
Reference implementation | Python · stats.empirical_distribution |
| Bayesian Updating | risk.capability.universal-mathematics-statistics.bayesian.updating.v1 |
Reference implementation | Julia · frontier_risk_math.beta_binomial |
| Hierarchical Bayesian | risk.capability.universal-mathematics-statistics.hierarchical.bayesian.v1 |
Adapter required | – |
| Extreme Value | risk.capability.universal-mathematics-statistics.extreme.value.v1 |
Reference implementation | – |
| Tail Risk | risk.capability.universal-mathematics-statistics.tail.risk.v1 |
Reference implementation | Python · tail.pot_gpd |
| Copula Dependency | risk.capability.universal-mathematics-statistics.copula.dependency.v1 |
Adapter required | – |
| Tail Dependence | risk.capability.universal-mathematics-statistics.tail.dependence.v1 |
Adapter required | Python · dependence.tail_empirical |
| Monte Carlo | risk.capability.universal-mathematics-statistics.monte.carlo.v1 |
Reference implementation | Python · simulation.monte_carlo |
| Latin Hypercube | risk.capability.universal-mathematics-statistics.latin.hypercube.v1 |
Reference implementation | Python · sampling.latin_hypercube |
| Importance Sampling | risk.capability.universal-mathematics-statistics.importance.sampling.v1 |
Reference implementation | Python · sampling.importance |
| Sequential Monte Carlo | risk.capability.universal-mathematics-statistics.sequential.monte.carlo.v1 |
Adapter required | – |
| Kalman Filter | risk.capability.universal-mathematics-statistics.kalman.filter.v1 |
Adapter required | – |
| Extended Kalman | risk.capability.universal-mathematics-statistics.extended.kalman.v1 |
Adapter required | – |
| Unscented Kalman | risk.capability.universal-mathematics-statistics.unscented.kalman.v1 |
Adapter required | – |
| Particle Filter | risk.capability.universal-mathematics-statistics.particle.filter.v1 |
Adapter required | – |
| Markov Chain | risk.capability.universal-mathematics-statistics.markov.chain.v1 |
Adapter required | Python · markov.transition |
| Hidden Markov | risk.capability.universal-mathematics-statistics.hidden.markov.v1 |
Adapter required | – |
| Change Point | risk.capability.universal-mathematics-statistics.change.point.v1 |
Reference implementation | Python · change.cusum |
| Anomaly Detection | risk.capability.universal-mathematics-statistics.anomaly.detection.v1 |
Reference implementation | Python · anomaly.robust_mad |
| Survival Analysis | risk.capability.universal-mathematics-statistics.survival.analysis.v1 |
Adapter required | Python · survival.kaplan_meier |
| Reliability | risk.capability.universal-mathematics-statistics.reliability.v1 |
Adapter required | Python · reliability.weibull |
| Fault Tree | risk.capability.universal-mathematics-statistics.fault.tree.v1 |
Adapter required | – |
| Event Tree | risk.capability.universal-mathematics-statistics.event.tree.v1 |
Adapter required | – |
| Bayesian Network | risk.capability.universal-mathematics-statistics.bayesian.network.v1 |
Adapter required | – |
| Information Theory | risk.capability.universal-mathematics-statistics.information.theory.v1 |
Reference implementation | Python · information.mutual_information |
| Entropy and Concentration | risk.capability.universal-mathematics-statistics.entropy.and.concentration.v1 |
Reference implementation | Julia · frontier_risk_math.concentration |
| Correlation | risk.capability.universal-mathematics-statistics.correlation.v1 |
Reference implementation | Python · stats.correlation |
| Regression | risk.capability.universal-mathematics-statistics.regression.v1 |
Adapter required | Python · regression.ols |
| Gaussian Process | risk.capability.universal-mathematics-statistics.gaussian.process.v1 |
Adapter required | Python · gaussian_process.rbf |
| Spatial Statistics | risk.capability.universal-mathematics-statistics.spatial.statistics.v1 |
Adapter required | Python · spatial.morans_i |
| Geostatistics | risk.capability.universal-mathematics-statistics.geostatistics.v1 |
Adapter required | Python · geostatistics.ordinary_kriging |
| Bootstrap | risk.capability.universal-mathematics-statistics.bootstrap.v1 |
Reference implementation | Python · stats.bootstrap_mean_ci |
| Global Sensitivity | risk.capability.universal-mathematics-statistics.global.sensitivity.v1 |
Connected | Python · sensitivity.sobol_outputs |
| Shapley Sensitivity | risk.capability.universal-mathematics-statistics.shapley.sensitivity.v1 |
Adapter required | Python · sensitivity.shapley_coalitions |
| Optimisation under Uncertainty | risk.capability.universal-mathematics-statistics.optimization.under.uncertainty.v1 |
Adapter required | Python · optimization.robust_minimax |
| Multi-Criteria Decision | risk.capability.universal-mathematics-statistics.multi.criteria.decision.v1 |
Adapter required | Python · decision.weighted_mcda |
| Real Options | risk.capability.universal-mathematics-statistics.real.options.v1 |
Adapter required | Python · decision.real_option_binomial |
Methods, data and supports
- Method families
- robust statistics, Bayesian inference, extreme value, Monte Carlo, copula dependence, stochastic process, time series, change detection, survival reliability, spatial statistics, geostatistics, optimisation, decision analysis, information theory
- 分野
- cross-domain
- Compute planes
- Julia, TypeScript
- Source classes
- governed-observations, sealed-risk-data-cut
- Spatial supports
- point, object, grid, region, network
- Temporal supports
- instant, series, window, scenario
ステータス
The Global Risks Index の手法、構造、定義は準備段階の作業草案(2026 年)であり、レビューと意見募集のために配布されているもので、実施を目的としたものではありません。管理されたエビデンスの区切りから版が公表されるまで、値は一切表示されません。公表後の値には、区間、区分、出典、訂正の経路が付されます。シミュレーターは、公表された手法をあなたが選んだ数値で実行するものであり、NXS-GRX の値を表示することはありません。
NXS-GRX ではないもの
NXS-GRX は研究およびリスク分析の情報です。信用格付や ESG 格付ではなく、格付尺度を用いず、金融商品のベンチマークでも、公的統計でも、緊急警報や公式分類でも、助言でもありません。自然人の評価に用いてはなりません。リスククラスは公開された尺度上の区分であり、国や人の順位付けではありません。
機関と情報源
The Nexus Consortium is stewarded by three independent not-for-profit institutions: The Global Centre for Risk and Innovation (GCRI), through GCRI US in Washington, DC and GCRI Canada in Ontario, with GCRI Canada as Lead Institution, Consortium Secretariat and lead contracting organisation; The Global Risks Forum (GRF); and The Global Risks Alliance (GRA). Technology, infrastructure and service providers work with the Consortium at arm’s length under written agreements, like every other external party in the public-private-planet model, and hold no role in its stewardship.
すべての値には情報源の帰属表示が付され、情報源はそれぞれ独自のライセンスを保持します。外部の名称は相互運用性のためにのみ引用しています。Nexus が執筆したテキストは、公開時に CC BY 4.0 とする予定です。
Avis en français
La méthodologie et les définitions du NXS-GRX sont un projet de travail préparatoire (2026). Le NXS-GRX est une information de recherche et d’analyse des risques : ni notation, ni indice de référence financier, ni statistique officielle, ni alerte, ni conseil. Les classes de risque sont des bandes sur une échelle publiée, non des classements.