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Bayesian Nonparametric Models
"... A Bayesian nonparametric model is a Bayesian model on an infinitedimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions, a ..."
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Cited by 16 (0 self)
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A Bayesian nonparametric model is a Bayesian model on an infinitedimensional parameter space. The parameter space is typically chosen as the set of all possible solutions for a given learning problem. For example, in a regression problem the parameter space can be the set of continuous functions
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
A tutorial on Bayesian nonparametric models
 Journal of Mathematical Psychology
"... 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 we describ ..."
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Cited by 39 (8 self)
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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. 1
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 16 (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
Bayesian Nonparametric Modelling of Spatial Data
"... In modelling spatial data there is often the need to use models that are able to capture some form of correlation between some variables. For example, we may want to introduce spatial dependence in the rates of occurrence of an ..."
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In modelling spatial data there is often the need to use models that are able to capture some form of correlation between some variables. For example, we may want to introduce spatial dependence in the rates of occurrence of an
Bayesian Nonparametric Modeling of Suicide Attempts
"... The National Epidemiologic Survey on Alcohol and Related Conditions (NESARC) database contains a large amount of information, regarding the way of life, medical conditions, etc., of a representative sample of the U.S. population. In this paper, we are interested in seeking the hidden causes behind ..."
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Cited by 2 (1 self)
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the suicide attempts, for which we propose to model the subjects using a nonparametric latent model based on the Indian Buffet Process (IBP). Due to the nature of the data, we need to adapt the observation model for discrete random variables. We propose a generative model in which the observations are drawn
Bayesian nonparametric models for prediction in networks
"... Why model networks? Many datasets take the form of networks or graphs... Social networks have binary (is friend, follows) or integer (retweets, shares) edges. Email networks have integer (number of emails) edges. Biological networks may have binary, integer or realvalued edges. Sinead Williamson No ..."
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Nonparametric networks 2 / 25 Modeling networks: Prediction vs Description There are a number of reasons we might want to model networks... Network recovery: We may only have a noisy version of the underlying network. Description/characterization: We may wish to find a latent explanation of the network
Bayesian Nonparametric Models on Decomposable Graphs
"... Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found many applications in clustering while the Indian buffet process (IBP) is increasingly used to describe latent feature models. These models are attractive because they ensure exchangeability (over sam ..."
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Cited by 1 (0 self)
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Over recent years Dirichlet processes and the associated Chinese restaurant process (CRP) have found many applications in clustering while the Indian buffet process (IBP) is increasingly used to describe latent feature models. These models are attractive because they ensure exchangeability (over
Results 1  10
of
390,833