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Dirichlet and Related Processes
The Dirichlet process plays a dominant role as a prior in Bayesian nonparametrics leading to the development of a wide variety of inferential...
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Latent mixture modeling for clustered data
This article proposes a mixture modeling approach to estimating cluster-wise conditional distributions in clustered (grouped) data. We adapt the...
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sppmix: Poisson point process modeling using normal mixture models
This paper describes the package sppmix for the statistical environment R. The sppmix package implements classes and methods for modeling spatial...
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Likelihood-free approximate Gibbs sampling
Likelihood-free methods such as approximate Bayesian computation (ABC) have extended the reach of statistical inference to problems with...
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On Approximations of the Beta Process in Latent Feature Models: Point Processes Approach
In recent times, the beta process has been widely used as a nonparametric prior for different models in machine learning, including latent feature...
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Numerical Treatment of Multidimensional Stochastic, Competitive and Evolutionary Models
In this chapter, we present a computational study of multi-dimensional stochastic systems of differential equations. Especially competitive and...
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Multisite and Statespace Models
This is a large and complex chapter which, with recent computational developments and matrix methods, is becoming a fundamental model as it includes...
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Topic Detection: A Statistical Model and a Quali-Quantitative Method
This chapter aims at comparing and contrasting two approaches for the automatic detection of topics in texts that show interesting similarities and...
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Discussion of “Nonparametric Bayesian Inference in Applications”: Bayesian nonparametric methods in econometrics
The use of Bayesian nonparametrics models has increased rapidly over the last few decades driven by increasing computational power and the...
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The determination of uncertainty levels in robust clustering of subjects with longitudinal observations using the Dirichlet process mixture
In this paper we introduce a new method to the cluster analysis of longitudinal data focusing on the determination of uncertainty levels for cluster...
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Mixed Models
Mixed models extend the predictor \(\eta = {\boldsymbol{x}}'{\boldsymbol{\beta }}\)...
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Risk Assessment for Toxicity Experiments with Discrete and Continuous Outcomes: A Bayesian Nonparametric Approach
We present a Bayesian nonparametric modeling approach to inference and risk assessment for developmental toxicity studies. The primary objective of...
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Challenges in the Analysis of Neuroscience Data
In the last two decades, our understanding of the mechanisms underlying the functioning and disruption of the human brain has advanced considerably....
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Estimation of the Complexity of a Finite Mixture Distribution: From Well- to Less Known Methods
Mixture models occur in numerous settings including random and fixed effects models, clustering, deconvolution, empirical Bayes problems and many...
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Ferguson–Sethuraman Processes
The Ferguson–Sethuraman countable mixture representation of the Dirichlet process has emerged recently as a dominant source of develo** several new...
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Prior Processes and Their Applications Nonparametric Bayesian Estimation
This book presents a systematic and comprehensive treatment of various prior processes that have been developed over the past four decades for...
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Prior Processes: An Overview
In this chapter we give an overview of the various processes that have been developed in the literature during the last four decades, and how it has...
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Tailfree Processes
In view of the limitation of the Dirichlet process that it selects a discrete probability distribution with probability one, efforts were made to...
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Bayesian methods for dealing with missing data problems
Missing data, a common but challenging issue in most studies, may lead to biased and inefficient inferences if handled inappropriately. As a natural...
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Introductory Overview of Statistical Analysis of Microbiome Data
In this chapter, we first introduce and discuss the themes and statistical hypothesesStatistical hypotheses in human microbiome studies in...