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Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation
Matthew Francis Dixon
bayesian statistics / model riskResource overview

Model Validation of Agentic AI Systems: A POMDP-Based Framework for Belief-State, Forecast, and Policy Validation

Paper2026

by Matthew Francis Dixon

Tags
bayesian statisticsmodel riskrisk management

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Suggested foundations for risk measurement and stress and probability and statistical reasoning. Specialist prerequisites vary by paper.

Before you read

  1. Random Variables and Distributions

    Read the distributions and expectations used in probabilistic arguments.

  2. Expected Shortfall and Other Tail Risk Measures

    Distinguish a loss quantile from the average loss in the tail.

  3. Conditional Expectation

    Interpret expectations after information is observed.

Go further

  1. VaR Estimation in Practice: Variance-Covariance, Historical Simulation, Monte Carlo

    Compare risk estimators and their assumptions.

  2. Backtesting VaR and ES: Coverage Tests and Model Validation

    Check forecasts against realised losses.

  3. Confidence Intervals and Hypothesis Testing

    Assess uncertainty rather than treating an estimate as a fact.

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