Results 11  20
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8,301
A tutorial on Bayesian nonparametric models.
 Journal of Mathematical Psychology,
, 2012
"... Abstract A key problem in statistical modeling is model selection, how to choose a model at an appropriate level of complexity. This problem appears in many settings, most prominently in choosing the number of clusters in mixture models or the number of factors in factor analysis. In this tutorial ..."
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Cited by 42 (9 self)
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we describe Bayesian nonparametric methods, a class of methods that sidesteps this issue by allowing the data to determine the complexity of the model. This tutorial is a highlevel introduction to Bayesian nonparametric methods and contains several examples of their application.
A Language Modeling Approach to Information Retrieval
, 1998
"... Models of document indexing and document retrieval have been extensively studied. The integration of these two classes of models has been the goal of several researchers but it is a very difficult problem. We argue that much of the reason for this is the lack of an adequate indexing model. This sugg ..."
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Cited by 1154 (42 self)
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an approach to retrieval based on probabilistic language modeling. We estimate models for each document individually. Our approach to modeling is nonparametric and integrates document indexing and document retrieval into a single model. One advantage of our approach is that collection statistics which
Nonparametric models can be checked 1
, 2013
"... Muller and Mitra present an excellent motivation and overview of Bayesian nonparametric models, and in fact their article could have gone on longer, to include models such as Bayesian additive regression trees (Chipman, George, and McCulloch, 2010) which have the potential to revolutionize the pract ..."
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Muller and Mitra present an excellent motivation and overview of Bayesian nonparametric models, and in fact their article could have gone on longer, to include models such as Bayesian additive regression trees (Chipman, George, and McCulloch, 2010) which have the potential to revolutionize
Bayesian nonparametric models of genetic variation
, 2015
"... We will develop three new Bayesian nonparametric models for genetic variation. These models are all dynamicclustering approximations of the ancestral recombination graph (or ARG), a structure that fully describes the genetic history of a population. Due to its complexity, ecient inference for the A ..."
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We will develop three new Bayesian nonparametric models for genetic variation. These models are all dynamicclustering approximations of the ancestral recombination graph (or ARG), a structure that fully describes the genetic history of a population. Due to its complexity, ecient inference
Measuring and testing the impact of news on volatility
, 1991
"... This paper introduces the News Impact Curve to measure how new information is incorporated into volatility estimates. A variety of new and existing ARCH models are compared and estimated with daily Japanese stock return data to determine the shape of the News Impact Curve. New diagnostic tests are p ..."
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Cited by 726 (14 self)
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are presented which emphasize the asymmetry of the volatility response to news. A partially nonparametric ARCH model is introduced to allow the data to estimate this shape. A comparison of this model with the existing models suggests that the best models are one by Glosten Jaganathan and Runkle (GJR
Projection Pursuit Regression
 Journal of the American Statistical Association
, 1981
"... A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures, ..."
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Cited by 550 (6 self)
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A new method for nonparametric multiple regression is presented. The procedure models the regression surface as a sum of general smooth functions of linear combinations of the predictor variables in an iterative manner. It is more general than standard stepwise and stagewise regression procedures
Bayesian Nonparametric Modeling for Causal Inference
, 2007
"... Researchers have long struggled to identify causal effects in nonexperimental settings. Many recentlyproposed strategies assume ignorability of the treatment assignment mechanism and require fitting two models – one for the assignment mechanism and one for the response surface. We propose a strate ..."
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Cited by 18 (2 self)
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strategy that instead focuses on very flexibly modeling just the response surface using a Bayesian nonparametric modeling procedure, Bayesian Additive Regression Trees (BART). BART has several advantages: it is far simpler to use than many recent competitors, requires less guesswork in model fitting
Learning nonparametric models for probabilistic imitation
 in Advances in Neural Information Processing Systems 19 (NIPS’06
, 2007
"... Learning by imitation represents an important mechanism for rapid acquisition of new behaviors in humans and robots. A critical requirement for learning by imitation is the ability to handle uncertainty arising from the observation process as well as the imitator’s own dynamics and interactions with ..."
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Cited by 17 (2 self)
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. Rather than relying on a known forward model of the dynamics, our approach learns a nonparametric forward model via exploration. Leveraging advances in approximate inference in graphical models, we show how the learned forward model can be directly used to plan an imitating sequence. We provide
Hierarchical Dirichlet processes.
 Journal of the American Statistical Association,
, 2006
"... We consider problems involving groups of data where each observation within a group is a draw from a mixture model and where it is desirable to share mixture components between groups. We assume that the number of mixture components is unknown a priori and is to be inferred from the data. In this s ..."
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Cited by 942 (78 self)
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. In this setting it is natural to consider sets of Dirichlet processes, one for each group, where the wellknown clustering property of the Dirichlet process provides a nonparametric prior for the number of mixture components within each group. Given our desire to tie the mixture models in the various groups, we
On the Testability of Identification in Some Nonparametric Models with Endogeneity
, 2013
"... This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models with endogenei ..."
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Cited by 11 (1 self)
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This paper examines three distinct hypothesis testing problems that arise in the context of identification of some nonparametric models with endogeneity. The first hypothesis testing problem we study concerns testing necessary conditions for identification in some nonparametric models
Results 11  20
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8,301