We study the revelation principle in multistage games for perfect Bayesian equilibium and sequential equilibrium. The question of whether or not the revelation principle holds is subtle, as expanding players’ opportunities for communication expands the set of consistent beliefs at off-path information sets. In settings with adverse selection but no moral hazard, Nash, perfect Bayesian, and sequ...

uncertainty associated with mixing models is often substantial, but has not yet been fully incorporated in models. the objective of this study is to develop and apply a bayesian-mixing model that estimates probability distributions of source contributions to a mixture associated with multiple sources for assessing the uncertainty estimation in sediment fingerprinting in zidasht catchment, iran....

In this paper, a class of Bayesian hierarchical disease mapping models with spline smoothing are motivated and developed for sequential disease mapping and for surveillance of disease risk trends and clustering. The methodological development aims to provide reliable information about the patterns (both over space and time) of disease risk and to quantify uncertainty. Bayesian disease mapping m...

Statistical methods of inference typically require the likelihood function to be computable in a reasonable amount of time. The class of “likelihood-free” methods termed Approximate Bayesian Computation (ABC) is able to eliminate this requirement, replacing the evaluation of the likelihood with simulation from it. Likelihood-free methods have gained in efficiency and popularity in the past few ...

Methods of Approximate Bayesian computation (ABC) are increasingly used for analysis of complex models. A major challenge for ABC is over-coming the often inherent problem of high rejection rates in the accept/reject methods based on prior:predictive sampling. A number of recent developments aim to address this with extensions based on sequential Monte Carlo (SMC) strategies. We build on this h...

We describe an information-theoretic approach to the analysis of sequential data, which emphasises the predictive aspects of perception, and the dynamic process of forming and modifying expectations about an unfolding stream of data, characterising these using a set of process information measures. After reviewing the theoretical foundations and the definition of the predictive information rate...