Chain Event Graphs (Chapman & Hall/CRC Computer Science & Data Analysis)

Hardcover, xx + 233 pages, NOT ex-library. Interior is clean and bright throughout with unmarked text, free of inscriptions and stamps, firmly bound. Boards show short creases to corners. Issued without a dust jacket. -- Contents: 1. Introduction [Some motivation; Why event trees?; Using event trees to describe populations; How we have arranged the material in this book; Exercises] 2. Bayesian inference using graphs [Inference on discrete statistical models (Two common sampling mass functions; Two prior-to-posterior analyses; Poisson-Gamma and Multinomial-Dirichlet; MAP model selection using Bayes Factors); Statistical models and structural hypotheses (An example of competing models; Parametric statistical model); Discrete Bayesian networks (Factorisations of probability mass functions; The d-separation theorem; DAGs coding the same distributional assumptions; Estimating probabilities in a BN; Propagating probabilities in a BN); Concluding remarks; Exercises]; 3. The Chain Event Graph [Models represented by tree graphs (Probability trees; Staged trees); The semantics of the Chain Event Graph; Comparison of stratified CEGs with BNs; Examples of CEG semantics (The saturated CEG; The simple CEG; The square-free CEG); Some related structures; Exercises]; 4. Reasoning with a CEG [Encoding qualitative belief structures with CEGs (Vertex- and edge-centred events; Intrinsic events; Conditioning in CEGs; Vertex-random variables, cuts and independence); CEG statistical models (Parametrised subsets of the probability simplex; The swap operator; The resize operator; The class of all statistically equivalent staged trees); Exercises]; 5. Estimation and propagation on a given CEG [Estimating a given CEG (A conjugate analysis; How to specify a prior for a given CEG; Example: learning liver and kidney disorders; When sampling is not random); Propagating information on trees and CEGs (Propagation when probabilities are known; Example: propagation for liver and kidney disorders; Propagation when probabilities are estimated; Some final comments); Exercises]; 6. Model selection for CEGs [Calibrated priors over classes of CEGs; Log-posterior Bayes Factor (lpBF) scores; CEG greedy and dynamic programming search (Greedy SCEG search using AHC; SCEG exhaustive search using DP); Technical advances for SCEG model selection (DP and AHC using a block ordering; A pairwise moment non-local prior); Exercises]; 7. How to model with a CEG: a real-world application [Previous studies and domain knowledge; Searching the CHDS dataset with a variable order; Searching the CHDS dataset with a block ordering; Searching the CHDS dataset without a variable ordering; Issues associated with model selection (Exhaustive CEG model search; Searching the CHDS dataset using NLPs; Setting a prior probability distribution); Exercise]; 8. Causal inference using CEGs [Bayesian networks and causation (Extending a BN to a causal BN; Problems of describing causal hypotheses using a BN); Defining a do-operation for CEGs (Composite manipulations; Example: student housing situation; Some special manipulations of CEGs); Causal CEGs (When a CEG can legitimately be called causal; Example: manipulations of the CHDS; Backdoor theorems); Causal discovery algorithms for CEGs; Exercises]; References; Index.…