000			04010nam a22003738i 4500
001			CR9781108277495
003			UkCbUP
005			20200124160341.0
006			m|||||o||d||||||||
007			cr||||||||||||
008			170111s2017||||enk o ||1 0|eng|d
020			_a9781108277495 (ebook)
020			_z9781108404969 (paperback)
040			_aUkCbUP _beng _erda _cUkCbUP
050		4	_aQA279 _b.R68 2017
082	0	4	_a519.538 _223
100	1		_aRoverato, Alberto, _eauthor.
245	1	0	_aGraphical models for categorical data / _cAlberto Roverato.
264		1	_aCambridge : _bCambridge University Press, _c2017.
300			_a1 online resource (vii, 152 pages) : _bdigital, PDF file(s).
336			_atext _btxt _2rdacontent
337			_acomputer _bc _2rdamedia
338			_aonline resource _bcr _2rdacarrier
490	1		_aSemStat elements
490	1		_aCambridge elements
500			_aTitle from publisher's bibliographic system (viewed on 29 May 2018).
520			_aFor advanced students of network data science, this compact account covers both well-established methodology and the theory of models recently introduced in the graphical model literature. It focuses on the discrete case where all variables involved are categorical and, in this context, it achieves a unified presentation of classical and recent results.
505	0	0	_aMachine generated contents note: _g1. _tIntroduction -- _g1.1. _tGraphical Models -- _g1.2. _tOutline of the Book -- _g1.2.1. _tDiscrete Graphical Models and Their Parameterization -- _g1.2.2. _tBinary vs Non-binary Variables -- _g2. _tConditional Independence and Cross-product Ratios -- _g2.1. _tNotation and Terminology -- _g2.1.1. _tCross-classified Tables -- _g2.2. _tConditional Independence -- _g2.3. _tEstablishing Independence Relationships -- _g3. _tMobius Inversion -- _g3.1. _tPreliminaries -- _g3.1.1. _tNotation and Terminology -- _g3.1.2. _tThe Zeta and the Mobius Matrices -- _g3.2. _tThe Mobius Inversion Formula -- _g3.2.1. _tTwo Basic Lemmas -- _g3.3. _tMobius Inversion and Partially Ordered Sets -- _g4. _tUndirected Graph Models -- _g4.1. _tGraphs -- _g4.2. _tMarkov Properties for Undirected Graphs -- _g4.3. _tThe Log-linear Parameterization -- _g4.4. _tHierarchical Log-linear Models -- _g4.5. _tLog-linear Graphical Models -- _g4.6. _tData, Estimation and Testing -- _g4.7. _tGraph Decomposition and Decomposable Graphs -- _g4.8. _tLocal Computation Properties -- _g4.9. _tModels for Decomposable Graphs -- _g4.10. _tLog-linear Models and the Exponential Family -- _g4.10.1. _tBasic Facts on the Theory of the Exponential Family -- _g4.10.2. _tThe Cross-classified Bernoulli Distribution -- _g4.10.3. _tExponential Family Representations of the Saturated Model -- _g4.10.4. _tExponential Family Representation of Hierarchical Log-linear Models -- _g4.11. _tModular Structure of the Asymptotic Variance of ML Estimates -- _g4.11.1. _tThe Variance Function and the Asymptotic Variance of ML Estimates -- _g4.11.2. _tVariances in the Saturated Model -- _g4.11.3. _tVariances in Hierarchical Log-linear Models -- _g4.11.4. _tDecompositions and Decomposable Models -- _g5. _tBidirected Graph Models -- _g5.1. _tBidirected Graphs -- _g5.2. _tMarkov Properties for Bidirected Graphs -- _g5.3. _tThe Log-mean Linear Parameterization -- _g5.4. _tLog-mean Linear Graphical Models -- _g5.5. _tExample: Symptoms in Psychiatric Patients -- _g5.6. _tParsimonious Graphical Modeling -- _g6. _tDirected Acyclic and Regression Graph Models -- _g6.1. _tDirected Acyclic Graphs -- _g6.2. _tMarkov Properties for Directed Acyclic Graphs -- _g6.3. _tRegression Graphs -- _g6.4. _tMarkov Properties for Regression Graphs -- _g6.5. _tOn the Interpretation of Models defined by Regression Graphs -- _g6.6. _tThe Log-hybrid Linear Parameterization -- _g6.7. _tLog-hybrid Linear Graphical Models -- _g6.8. _tInference in Regression Graph Models.
650		0	_aGraphical modeling (Statistics)
776	0	8	_iPrint version: _z9781108404969
830		0	_aSemStat elements.
830		0	_aCambridge elements.
856	4	0	_uhttps://doi.org/10.1017/9781108277495
999			_c523492 _d523490