One of the core problems of modern statistics is to approximate difficult-to-compute
probability densities. This problem is especially important in Bayesian statistics, which …

In this paper, we provide a comprehensive survey of the mixture of experts (ME). We discuss
the fundamental models for regression and classification and also their training with the …

Simultaneous localization, map** and moving object tracking (SLAMMOT) involves both
simultaneous localization and map** (SLAM) in dynamic environments and detecting and …

Bayesian statistical practice makes extensive use of versions of objective Bayesian analysis.
We discuss why this is so, and address some of the criticisms that have been raised …

Two major goals in machine learning are the discovery and improvement of solutions to
complex problems. In this paper, we argue that complexification, ie the incremental …

We provide statistical guarantees for a family of variational approximations to Bayesian
posterior distributions, called α-VB, which has close connections with variational …

Neural networks (NNs) and decision trees (DTs) are both popular models of machine
learning, yet coming with mutually exclusive advantages and limitations. To bring the best of …

Résumé This work is on Gaussian-process based approximation of a code which can be run
at different levels of accuracy. The goal is to improve the predictions of a surrogate model of …

Models such as feed-forward neural networks and certain other structures investigated in the
computer science literature are not amenable to closed-form Bayesian analysis. The paper …

Bayesian estimation of the parameters in beta mixture models (BMM) is analytically
intractable. The numerical solutions to simulate the posterior distribution are available, but …