The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …

We propose a general framework for the estimation of observables with generative neural
samplers focusing on modern deep generative neural networks that provide an exact …

This extraordinary three-volume work, written in an engaging and rigorous style by a world
authority in the field, provides an accessible, comprehensive introduction to the full spectrum …

Fundamental theory and practical algorithms of weakly supervised classification,
emphasizing an approach based on empirical risk minimization. Standard machine learning …

This work takes a critical look at the application of conventional machine learning methods
to wireless communication problems through the lens of reliability and robustness. Deep …

In this paper, the state estimation problems of nonlinear systems with outlier-corrupted
measurements are investigated. First, to model the non-Gaussian noises caused by the …

This article explores the problem of nonlinear state estimation in the presence of outlier-
contaminated measurements. First, to deal with the non-stationary non-Gaussian noises …

Saturated diagnostic classification models (DCM) can flexibly accommodate various
relationships among attributes to diagnose individual attribute mastery, and include various …

In statistical inference, uncertainty is unknown and all models are wrong. That is to say, a
person who makes a statistical model and a prior distribution is simultaneously aware that …

This article is a review of theoretical advances in the research field of algebraic geometry
and Bayesian statistics in the last two decades. Many statistical models and learning …