Computational Business Analytics

ISBN: 9781439890707 出版年：2013 页码：506 Das, Subrata CRC Press

Analytics Background and Architectures ANALYTICS DEFINED ANALYTICS MODELING ANALYTICS PROCESSES ANALYTICS AND DATA FUSION Mathematical and Statistical Preliminaries STATISTICS AND PROBABILITY THEORY LINEAR ALGEBRA FUNDAMENTALS MATHEMATICAL LOGIC GRAPHS AND TREES MEASURES OF PERFORMANCE ALGORITHMIC COMPLEXITY Statistics for Descriptive Analytics PROBABILITY DISTRIBUTIONS DISCRETE PROBABILITY DISTRIBUTIONS CONTINUOUS PROBABILITY DISTRIBUTIONS GOODNESS-OF-FIT TEST Bayesian Probability and Inference BAYESIAN INFERENCE PRIOR PROBABILITIES Inferential Statistics and Predictive Analytics CHISQUARE TEST OF INDEPENDENCE REGRESSION ANALYSES BAYESIAN LINEAR REGRESSION PRINCIPAL COMPONENT AND FACTOR ANALYSES SURVIVAL ANALYSIS AUTOREGRESSION MODELS Artificial Intelligence for Symbolic Analytics ANALYTICS AND UNCERTAINTIES NEO-LOGICIST APPROACH NEO-PROBABILIST NEO-CALCULIST APPROACH NEO-GRANULARIST Probabilistic Graphical Modeling NAIVE BAYESIAN CLASSIFIER (NBC) KDEPENDENCE NAIVE BAYESIAN CLASSIFIER (KNBC) BAYESIAN BELIEF NETWORKS Decision Support and Prescriptive Analytics EXPECTED UTILITY THEORY AND DECISION TREES INFLUENCE DIAGRAMS FOR DECISION SUPPORT SYMBOLIC ARGUMENTATION FOR DECISION SUPPORT Time Series Modeling and Forecasting PROBLEM MODELING KALMAN FILTER (KF) MARKOV MODELS DYNAMIC BAYESIAN NETWORKS (DBNS) Monte Carlo Simulation MONTE CARLO APPROXIMATION GIBBS SAMPLING METROPOLIS-HASTINGS ALGORITHM PARTICLE FILTER (PF) Cluster Analysis and Segmentation HIERARCHICAL CLUSTERING K-MEANS CLUSTERING K-NEAREST NEIGHBORS SUPPORT VECTOR MACHINES NEURAL NETWORKS Machine Learning for Analytics Models DECISION TREES LEARNING NAIVE BAYESIAN CLASSIFIERS LEARNING OF KNBC BAYESIAN BELIEF NETWORKS INDUCTIVE LOGIC PROGRAMMING Unstructured Data and Text Analytics INFORMATION STRUCTURING AND EXTRACTION BRIEF INTRODUCTION TO NLP TEXT CLASSIFICATION AND TOPIC EXTRACTION Semantic Web RESOURCE DESCRIPTION FRAMEWORK (RDF) DESCRIPTION LOGICS Analytics Tools INTELLIGENT DECISION AIDING SYSTEM (IDAS) ENVIRONMENT FOR FIFTH GENERATION APPLICATIONS (E5) ANALYSIS OF TEXT (ATEXT) R AND MATLAB SAS AND WEKA Analytics Case Studies RISK ASSESSMENT MODEL I3 RISK ASSESSMENT IN INDIVIDUAL LENDING USING IDAS RISK ASSESSMENT IN COMMERCIAL LENDING USING E5 AND IDAS FRAUD DETECTION SENTIMENT ANALYSIS USING ATEXT Appendix A: Usage of Symbols Appendix B: Examples and Sample Data Appendix C: MATLAB and R Code Examples Index Further Reading appears at the end of each chapter.