We provide a comprehensive overview of latent Markov (LM) models for the analysis of
longitudinal categorical data. We illustrate the general version of the LM model which …

Emphasizing the use of WinBUGS and R to analyze real data, Bayesian Ideas and Data
Analysis: An Introduction for Scientists and Statisticians presents statistical tools to address …

Multinomial processing tree models are widely used in many areas of psychology. A
hierarchical extension of the model class is proposed, using a multivariate normal …

This book presents a philosophical approach to probability and probabilistic thinking,
considering the underpinnings of probabilistic reasoning and modeling, which effectively …

Synthetic gene drives could provide new solutions to a range of old problems such as
controlling vector-borne diseases, agricultural pests and invasive species. In this paper, we …

The parameters of a discrete stationary Markov model are transition probabilities between
states. Traditionally, data consist in sequences of observed states for a given number of …

The Generalized Pareto (GP) and Generalized extreme value (GEV) distributions play an
important role in extreme value analyses as models for threshold excesses and block …

Non-Bayesian social learning theory provides a framework that models distributed inference
for a group of agents interacting over a network. Agents iteratively form and communicate …

Praise for Bayesian Thinking in Biostatistics:" This thoroughly modern Bayesian book… is
a'must have'as a textbook or a reference volume. Rosner, Laud and Johnson make the case …

This stand-alone tutorial gives an introduction to the QTest 2.1 public domain software
package for the specification and statistical analysis of certain order-constrained …