Bayesian Methods for Calibrating Health Policy Models: A Tutorial

A Bayesian Approach for Calibrating Risk Assessment Models

Multigrid Methods for Calibrating Financial Models

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Bayesian models of perception: a tutorial introduction

Bayesian approaches to perception offer a principled, coherent and elegant answer to the central problem of perception: what the brain should believe about the world based on sensory data. This chapter gives a tutorial introduction to Bayesian inference, illustrating how it has been applied to problems in perception. Inference in perception One of the central ideas in the study of perception is...

Calibrating Subjective Probabilities Using Hierarchical Bayesian Models

Abstract. A body of psychological research has examined the correspondence between a judge’s subjective probability of an event’s outcome and the event’s actual outcome. The research generally shows that subjective probabilities are noisy and do not match the “true” probabilities. However, subjective probabilities are still useful for forecasting purposes if they bear some relationship to true ...

A tutorial on Bayesian inference for variable dimension models

Variable dimension models are problems where the parameter space is not well defined, therefore the sample space is a infinite collection of unrelated subspaces. If the considered statistical model is not defined in concise way, then the dimensionality of the parameter space can also be part of the model uncertainty. These problems have been studied in the context of Bayesian model comparison a...