Approximate Bayesian inference on the basis of summary statistics is wellsuited to complex problems for which the likelihood is either mathematically or computationally intractable. However the methods that use rejection suffer from the curse of dimensionality when the number of summary statistics is increased. Here we propose a machine-learning approach to the estimation of the posterior densi...

We present a novel approach for constrained Bayesian inference. Unlike current methods, our approach does not require convexity of the constraint set. We reduce the constrained variational inference to a parametric optimization over the feasible set of densities and propose a general recipe for such problems. We apply the proposed constrained Bayesian inference approach to multitask learning su...

Various aspects of Bayesian inference in selection and size biased sampling problems are presented beginning with discussion of general problems of inference in in nite and nite populations subject to selection sampling Estimation of the size of nite populations and inference about superpopulation distributions when sampling is apparently informative is then developed in two speci c problems Th...

In many engineering problems, unknown parameters of a model are inferred in order to make predictions, to design controllers, or to optimize the model. When parameters are distributed (continuous) or very high-dimensional (discrete) and quantities of interest are low-dimensional, parameters need not be fully resolved to make accurate estimates of quantities of interest. In this work, we extend ...

The Bayesian approach to hierarchical linear models has many advantages when compared with likelihood-based methods. Initially, the clear advantage has been with robust inference in small sample settings. But more recent approaches to Bayesian computations, based on Markov Chain Monte Carlo (MCMC) simulation, have vastly improved the viability of Bayesian models in practice. Many of the newer a...

A new methodology has been developed to design optimal monitoring network to estimate the transient pollution plume resulting from active pollution sources in a contaminated aquifer. An optimization algorithm is linked with geostatistical kriging model as well as a numerical simulation model. Simulated annealing is used as the optimization tool. The physical process in the aquifer, i.e., the fl...

Abstract: This paper proposes a novel scheme for multi-static passive radar processing, based on soft-input soft-output processing and Bayesian sparse estimation. In this scheme, each receiver estimates the probability of target presence based on its received signal and the prior information received from a central processor. The resulting posterior target probabilities are transmitted to the c...

the problems of sequential change-point have several important applications in quality control, signal processing, and failure detection in industry and finance. we discuss a bayesian approach in the context of statistical process control: at an unknown time $tau$, the process behavior changes and the distribution of the data changes from p0 to p1. two cases are considered: (i) p0 and p1 are fu...

In recent years important progress has been achieved towards proving the validity of the replica predictions for the (asymptotic) mutual information (or “free energy”) in Bayesian inference problems. The proof techniques that have emerged appear to be quite general, despite they have been worked out on a case-by-case basis. Unfortunately, a common point between all these schemes is their relati...