Stochastic processes are widely used for model building in the social, physical, engineering
and life sciences as well as in financial economics. In model building, statistical inference for …

This book has grown out of several courses that we have given over the years at Purdue
University, Michigan State University and the Indian Statistical Institute on Bayesian …

This is a graduate-level textbook on Bayesian analysis blending modern Bayesian theory,
methods, and applications. Starting from basic statistics, undergraduate calculus and linear …

Parameter estimation in stochastic differential equations and stochastic partial differential
equations is the science, art and technology of modelling complex phenomena and making …

This paper reconciles the asymptotic disagreement between Bayesian and frequentist
inference in set‐identified models by adopting a multiple‐prior (robust) Bayesian approach …

Generalized likelihoods are commonly used to obtain consistent estimators with attractive
computational and robustness properties. Formally, any generalized likelihood can be used …

In this book, we study stochastic volatility models and methods of pricing, hedging, and
estimation. Among models, we will study models with heavy tails and long memory or long …

In one of his fundamental papers Le Can (1960) introduced what is now called locally
asymptotically normal (LAN) families of distributions and obtained several basic results …

Temperature estimation plays a vital role across natural sciences. A standard approach is
provided by probe thermometry, where a probe is brought into contact with the sample and …

In Gaussian sequence models with Gaussian priors, we develop some simple examples to
illustrate three perspectives on matching of posterior and frequentist probabilities when the …