© Institute of Mathematical Statistics, 2004 Nonparametric Bayesian Data Analysis

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Subfields: Artificial Intelligence, Statistics and Probability

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Citations in the corpus

Kernel Bayes' Rule (2010, methodology): “…seen when the prior or likelihood is given by a random process: Ap-proximate Bayesian Computation [23, 29, 39] is widely applied in population genetics, where the likelihood is given by a branching process, and nonparametric Bayesian inference [ 26] often uses a process prior with sampling methods.”
Bayesian inference in a sample selection model (2011, unknown): “1 To quote Muller and Quintana (2004) , “Nonparametric Bayesian inference is an oxymoron and a misnomer”.”
Pattern Recognition and Machine Learning (2006, unknown): “The reader should be aware, however, that nonparametric Bayesian methods are attracting increasing interest (Walker et al., 1999; Neal, 2000; Müller and Quintana, 2004; Teh et al., 2006).”
Sampling the Dirichlet Mixture Model with Slices (2007, background): “A recent survey is given in MacEachern (1998), and other papers in the book of Dey et al. (1998) and by Müller and Quintana (2004).”
Bayesian density regression (2007, unknown): “Sethuraman (1994) showed that this characterization is equivalent to assuming Gx ∼ DP (αG0,x), where DP (αG0) denotes the Dirichlet process centered on base measure G0 with precision α.”
Stick-breaking autoregressive processes (2011, methodology): “JEL classification: C11; C14; C22”
Changes in the Distribution of Income Volatility (2008, background): “See Muller and Quintana (2004) for a general introduction to Bayesian non-parametric modeling with the Dirichlet process.”
Inference for Mixtures of Finite Polya Tree Models (2006, background): “More generally, Müller and Quintana (2004) noted that semiparametric extensions can be used to “robustify” standard parametric hierarchical models; MPT generalizations are ideal for this because they are readily centered at a parametric family.”
A Bayesian Semiparametric Approach to Intermediate Variables in Causal Inference (2011, methodology): “To address this issue, we propose a Bayesian nonparametric model for continuous intermediate variables based on Dirichlet process mixture models (DPM) (e.g., Escobar and West, 1995; Müller and Quintana, 2004).”
Nonparametric bayesian multitask collaborative filtering (2013, methodology): “That paper essentially builds upon a previous work on Bayesian matrix factorization [24], where the user and item factor matrices are considered to be drawn from a Dirichlet process mixture prior [21], instead of simple multivariate Gaussians.”
A Flexible Bayesian Nonparametric Model for Predicting Future Insurance Claims (2017, unknown): “Mixture models are common in this context as well, and a widely used class of prior distributions for a density function is a Dirichlet process mixture; see, for example, Müller and Quintana (2004) and the references therein.”
Bayesian inference with dependent normalized completely random measures (2014, background): “Reviews and key references can be found in, for example, [8,29,37].”
Bayesian Nonparametrics: Models beyond the Dirichlet process (2009, unknown): “Among them we mention the discussion paper by Walker, Damien, Laud and Smith (1999), the book by Ghosh and Ramamoorthi (2003), the lecture notes by Regazzini (2001) and the review articles by Hjort (2003) and Müller and Quintana (2004).”
Dirichlet Process (2017, methodology): “In addition to the references embedded in the text above, we recommend the review articles [13, 14] on Bayesian nonparametrics and [15] on Dirichlet processes.”
Robustifying Generalized Linear Mixed Models Using a New Class of Mixtures of Multivariate Polya Trees (2009, methodology): “The DP was introduced as a prior probability model for random probability measures by Ferguson (1973), and has become the most widely used Bayesian nonparametric model (Müller and Quintana 2004).”
The Gibbs and splitmerge sampler for population mixture analysis from genetic data with incomplete baselines (2006, methodology): “In place of the finite mixture model, a nonparametric analogue called the Dirichlet process mixture model (Walker et al. 1999; Neal 2000; Müller and Quintana 2004) is the statistical foundation.”
Variational Russian Roulette for Deep Bayesian Nonparametrics (2019, methodology): “A principled method from statistics to choose the complexity of a model is provided by Bayesian nonparametric methods (Walker et al., 1999; Müller & Quintana, 2004; Orbanz & Teh, 2010; Gershman & Blei, 2012).”
Smoothly mixing regressions (2007, background): “Müller and Quintana (2004) provide an accessible review of the Bayesian nonparametric regression literature.”
Posterior Analysis for Normalized Random Measures with Independent Increments (2009, methodology): “…process by Ferguson (1973), various approaches for constructing random probability measures, whose distribution acts as a non-parametric prior, have been undertaken with the aim of overcoming some of the drawbacks of the Dirichlet process (see Müller and Quintana, 2004, for a recent review).”