The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

The Bayesian statistical paradigm provides a principled and coherent approach to
probabilistic forecasting. Uncertainty about all unknowns that characterize any forecasting …

In this article, we introduce the BART R package which is an acronym for Bayesian additive
regression trees. BART is a Bayesian nonparametric, machine learning, ensemble …

A comprehensive overview of Monte Carlo simulation that explores the latest topics,
techniques, and real-world applications More and more of today's numerical problems found …

Although interest in spatial regression models has surged in recent years, a comprehensive,
up-to-date text on these approaches does not exist. Filling this void, Introduction to Spatial …

Bayesian methods have become widespread in marketing literature. We review the essence
of the Bayesian approach and explain why it is particularly useful for marketing problems …

Bayesian methods are increasingly being used in the social sciences, as the problems
encountered lend themselves so naturally to the subjective qualities of Bayesian …

In this course we perform an introduction to Bayesian methods, we show some basic
definitions and properties of the bayesian approach. We have taken the content from the …

In this paper, we consider how rich sources of information on consumer choice can help to
identify demand parameters in a widely used class of differentiated products demand …

This paper studies computationally and theoretically attractive estimators called here
Laplace type estimators (LTEs), which include means and quantiles of quasi-posterior …