scholarly journals Nonparametric Bayesian Models for Unsupervised Scene Analysis and Reconstruction

2012 ◽  
Author(s):  
Dominik Joho ◽  
Gian Diego Tipaldi ◽  
Nikolas Engelhard ◽  
Cyrill Stachniss ◽  
Wolfram Burgard
Keyword(s):  
Bayesian Models ◽  
Scene Analysis ◽  
Nonparametric Bayesian ◽  
Nonparametric Bayesian Models

Nonparametric Bayesian models of categorization

2012 ◽  
pp. 173-198 ◽  
Author(s):  
Thomas L. Griffiths ◽  
Adam N. Sanborn ◽  
Kevin R. Canini ◽  
Daniel J. Navarro ◽  
Joshua B. Tenenbaum
Keyword(s):  
Bayesian Models ◽  
Nonparametric Bayesian ◽  
Nonparametric Bayesian Models

scholarly journals Local-Mass Preserving Prior Distributions for Nonparametric Bayesian Models

2014 ◽  
Vol 9 (2) ◽  
pp. 307-330 ◽  
Author(s):  
Juhee Lee ◽  
Steven N. MacEachern ◽  
Yiling Lu ◽  
Gordon B. Mills
Keyword(s):  
Bayesian Models ◽  
Nonparametric Bayesian ◽  
Prior Distributions ◽  
Local Mass ◽  
Nonparametric Bayesian Models

Structure and Flexibility in Bayesian Models of Cognition

2015 ◽  
Author(s):  
Joseph L. Austerweil ◽  
Samuel J. Gershman ◽  
Thomas L. Griffiths
Keyword(s):  
Sensory Input ◽  
Probabilistic Models ◽  
Bayesian Models ◽  
Future Research ◽  
Nonparametric Bayesian ◽  
Functional Relationships ◽  
Open Questions ◽  
Nonparametric Bayesian Models ◽  
Bayesian Models Of Cognition ◽  
Do So

Probability theory forms a natural framework for explaining the impressive success of people at solving many difficult inductive problems, such as learning words and categories, inferring the relevant features of objects, and identifying functional relationships. Probabilistic models of cognition use Bayes’s rule to identify probable structures or representations that could have generated a set of observations, whether the observations are sensory input or the output of other psychological processes. In this chapter we address an important question that arises within this framework: How do people infer representations that are complex enough to faithfully encode the world but not so complex that they “overfit” noise in the data? We discuss nonparametric Bayesian models as a potential answer to this question. To do so, first we present the mathematical background necessary to understand nonparametric Bayesian models. We then delve into nonparametric Bayesian models for three types of hidden structure: clusters, features, and functions. Finally, we conclude with a summary and discussion of open questions for future research.

scholarly journals Dynamic Nonparametric Bayesian Models for Analysis of Music

2010 ◽  
Vol 105 (490) ◽  
pp. 458-472 ◽  
Author(s):  
Lu Ren ◽  
David Dunson ◽  
Scott Lindroth ◽  
Lawrence Carin
Keyword(s):  
Bayesian Models ◽  
Nonparametric Bayesian ◽  
Nonparametric Bayesian Models
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