Bassist is a simulation tool for the analysis of complex statistical models. Given a high-level specification of a full probability model, Bassist generates a simulator for analysing the model with respect to given data. Bassist follows the Bayesian modeling approach. Model parameters and other unknown quantities are assigned prior distributions by the modeler; the likelihood of observed data f...

We demonstrate how to perform direct simulation from the posterior distribution of a class of multiple changepoint models where the number of changepoints is unknown. The class of models assumes independence between the posterior distribution of the parameters associated with segments of data between successive changepoints. This approach is based on the use of recursions, and is related to wor...

Thompson sampling provides a solution to bandit problems in which new observations are allocated to arms with the posterior probability that an arm is optimal. While sometimes easy to implement and asymptotically optimal, Thompson sampling can be computationally demanding in large scale bandit problems, and its performance is dependent on the model fit to the observed data. We introduce bootstr...

Consider the model in which the data consist of possibly censored lifetimes, and one puts a mixture of Dirichlet process priors on the common survival distribution. The exact computation of the posterior distribution of the survival function is in general impossible to obtain. This paper develops and compares the performance of several simulation techniques, based on Markov chain Monte Carlo an...

This article extends Bayarri and Berger’s (1999) proposal for model evaluation using “partial posterior” p values to the evaluation of second-stage model assumptions in hierarchical models. Applications focus on normal-normal hierarchical models, although the final example involves an application to a beta-binomial model in which the distribution of the test statistic is assumed to be approxima...

In this paper we present a fully coherent and consistent objective Bayesian analysis of the linear regression model using intrinsic priors. The intrinsic prior is a scaled mixture of g-priors and promotes shrinkage towards the subspace defined by a base (or null) model. While it has been established that the intrinsic prior provides consistent model selectors across a range of models, the poste...

The aim of this work is to investigate how to exploit the temporal information in a video sequence for the task of face recognition. Following the approach in [11], we propose a probabilistic model parameterized by a tracking state vector and a recognizing identity variable, simultaneously characterizing the kinematics and identity of humans. We then invoke a CONDENSATION [8] approach to provid...

We present a Bayesian statistical inference approach for simultaneously estimating mutation rate, population sizes, and migration rates in an island-structured population, using temporal and spatial sequence data. Markov chain Monte Carlo is used to collect samples from the posterior probability distribution. We demonstrate that this chain implementation successfully reaches equilibrium and rec...

The article is concerned with analysis of failure rate (shape parameter) under the Topp Leone distribution using a Bayesian framework. Different loss functions and a couple of noninformative priors have been assumed for posterior estimation. The posterior predictive distributions have also been derived. A simulation study has been carried to compare the performance of different estimators. A re...

Sequence alignment without the specification of gap penalties or a scoring matrix is attained by using Bayesian inference and a recursive algorithm. This procedure's recursive algorithm sums over all possible alignments on the forward step to obtain normalizing constants essential to Bayesian inferences, and samples from the exact posterior distribution on the backward step. Since both terminal...