Jump to ContentJump to Main Navigation

You are looking at 1-17 of 17 items

  • Keywords: graphical models x
Clear All Modify Search

View:

Theory Unification and Graphical Models in Human Categorization

David Danks

in Causal Learning: Psychology, Philosophy, and Computation

Published in print:
2007
Published Online:
April 2010
ISBN:
9780195176803
eISBN:
9780199958511
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780195176803.003.0012
Subject:
Psychology, Developmental Psychology

Many different, seemingly mutually exclusive, theories of categorization have been proposed in recent years. The most notable theories have been those based on prototypes, exemplars, and causal ... More


Some topics in social statistics

David Firth

in Celebrating Statistics: Papers in honour of Sir David Cox on his 80th birthday

Published in print:
2005
Published Online:
September 2007
ISBN:
9780198566540
eISBN:
9780191718038
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198566540.003.0008
Subject:
Mathematics, Probability / Statistics

This chapter summarizes recent themes and research topics in social statistics, viewed as statistical methods of particular value in substantive research fields such as criminology, demography, ... More


Potential effects of a keystone species on the dynamics of sylvatic plague

Chris Ray and Sharon K. Collinge

in Disease Ecology: Community structure and pathogen dynamics

Published in print:
2006
Published Online:
September 2007
ISBN:
9780198567080
eISBN:
9780191717871
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198567080.003.0014
Subject:
Biology, Disease Ecology / Epidemiology

Plague is emerging as a threat to humans and wildlife throughout western North America. Sylvatic plague, caused by the bacterium Yersinia pestis, is maintained within a network of mammal species and ... More


Approximating Max‐Sum‐Product Problems using Multiplicative Error Bounds

Christopher Meek and Ydo Wexler

in Bayesian Statistics 9

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199694587
eISBN:
9780191731921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199694587.003.0015
Subject:
Mathematics, Probability / Statistics

We describe the Multiplicative Approximation Scheme (MAS) for approximate inference in multiplicative models. We apply this scheme to develop the DynaDecomp approximation algorithm. This algorithm ... More


Modeling Linkage Disequilibrium with Decomposable Graphical Models

Haley J. Abel and Alun Thomas

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0010
Subject:
Mathematics, Probability / Statistics, Biostatistics

This chapter describes the use of decomposable graphical models (DGMs) to represent the dependences within genetic data, or linkage disequilibrium (LD), prior to various downstream applications. ... More


Moment Priors for Bayesian Model Choice with Applications to Directed Acyclic Graphs *

Guido Consonni and Luca La Rocca

in Bayesian Statistics 9

Published in print:
2011
Published Online:
January 2012
ISBN:
9780199694587
eISBN:
9780191731921
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780199694587.003.0004
Subject:
Mathematics, Probability / Statistics

We propose a new method for the objective comparison of two nested models based on non‐local priors. More specifically, starting with a default prior under each of the two models, we construct a ... More


Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Raphaël Mourad (ed.)

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
book
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.001.0001
Subject:
Mathematics, Probability / Statistics, Biostatistics

At the crossroads between statistics and machine learning, probabilistic graphical models provide a powerful formal framework to model complex data. Probabilistic graphical models are probabilistic ... More


Essentials to Understand Probabilistic Graphical Models: A Tutorial about Inference and Learning

Christine Sinoquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0002
Subject:
Mathematics, Probability / Statistics, Biostatistics

The aim of this chapter is to offer an advanced tutorial to scientists with no background or no deep background on probabilistic graphical models. To readers more familiar with these models, this ... More


Network Inference in Breast Cancer with Gaussian Graphical Models and Extensions

Marine Jeanmougin, Camille Charbonnier, Mickaël Guedj, and Julien Chiquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0005
Subject:
Mathematics, Probability / Statistics, Biostatistics

Clustering genes with high correlations will group genes with close expression profiles, defining clusters of co-expressed genes. However, such correlations do not provide any clue on the chain of ... More


Probabilistic Graphical Models for Next-generation Genomics and Genetics

Christine Sinoquet

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0001
Subject:
Mathematics, Probability / Statistics, Biostatistics

The explosion in omics and other types of biological data has increased the demand for solid, large-scale statistical methods. These data can be discrete or continuous, dependent or independent, from ... More


Graphical Models and Multivariate Analysis of Microarray Data

Harri Kiiveri

in Probabilistic Graphical Models for Genetics, Genomics, and Postgenomics

Published in print:
2014
Published Online:
December 2014
ISBN:
9780198709022
eISBN:
9780191779619
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198709022.003.0003
Subject:
Mathematics, Probability / Statistics, Biostatistics

The usual analysis of gene expression data ignores the correlation between gene expression values. Biologically, this assumption is unreasonable. The approach presented in this chapter allows for ... More


Statistical inference with probabilistic graphical models

Devavrat Shah

in Statistical Physics, Optimization, Inference, and Message-Passing Algorithms: Lecture Notes of the Les Houches School of Physics: Special Issue, October 2013

Published in print:
2015
Published Online:
March 2016
ISBN:
9780198743736
eISBN:
9780191803802
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198743736.003.0001
Subject:
Physics, Theoretical, Computational, and Statistical Physics

This chapter introduces graphical models as a powerful tool to derive efficient algorithms for inference problems. When dealing with complex interdependent variables, inference problems may become of ... More


Alternative Graphical Causal Models and the Identification of Direct Effects

James M. Robins and Thomas S. Richardson

in Causality and Psychopathology: Finding the Determinants of Disorders and their Cures

Published in print:
2011
Published Online:
November 2020
ISBN:
9780199754649
eISBN:
9780197565650
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780199754649.003.0011
Subject:
Clinical Medicine and Allied Health, Psychiatry

The subject-specific data from either an observational or experimental study consist of a string of numbers. These numbers represent a series of empirical measurements. Calculations are performed ... More


Advanced Sequence Analysis

Magy Seif El-Nasr, Truong Huy Nguyen Dinh, Alessandro Canossa, and Anders Drachen

in Game Data Science

Published in print:
2021
Published Online:
November 2021
ISBN:
9780192897879
eISBN:
9780191919466
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/oso/9780192897879.003.0011
Subject:
Computer Science, Human-Computer Interaction, Game Studies

This chapter discusses more advanced methods for sequence analysis. These include: probabilistic methods using classical planning, Bayesian Networks (BN), Dynamic Bayesian Networks (DBNs), Hidden ... More


A Taxonomy for Semi-Supervised Learning Methods

Seeger Matthias

in Semi-Supervised Learning

Published in print:
2006
Published Online:
August 2013
ISBN:
9780262033589
eISBN:
9780262255899
Item type:
chapter
Publisher:
The MIT Press
DOI:
10.7551/mitpress/9780262033589.003.0002
Subject:
Computer Science, Machine Learning

This chapter proposes a simple taxonomy of probabilistic graphical models for the semi-supervised learning (SSL) problem. It provides some broad classes of algorithms for each of the families and ... More


Cavity method: message-passing from a physics perspective

Marc Mézard

in Statistical Physics, Optimization, Inference, and Message-Passing Algorithms: Lecture Notes of the Les Houches School of Physics: Special Issue, October 2013

Published in print:
2015
Published Online:
March 2016
ISBN:
9780198743736
eISBN:
9780191803802
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198743736.003.0004
Subject:
Physics, Theoretical, Computational, and Statistical Physics

The cavity method is introduced as a heuristic framework from a physics perspective to solve probabilistic graphical models and is presented at both the replica symmetry (RS) and one-step replica ... More


Top-Down Predictions Determine Perceptions

Martin V. Butz and Esther F. Kutter

in How the Mind Comes into Being: Introducing Cognitive Science from a Functional and Computational Perspective

Published in print:
2017
Published Online:
July 2017
ISBN:
9780198739692
eISBN:
9780191834462
Item type:
chapter
Publisher:
Oxford University Press
DOI:
10.1093/acprof:oso/9780198739692.003.0009
Subject:
Psychology, Cognitive Models and Architectures, Cognitive Psychology

While bottom-up visual processing is important, the brain integrates this information with top-down, generative expectations from very early on in the visual processing hierarchy. Indeed, our brain ... More


View: