About the Book
Please note that the content of this book primarily consists of articles available from Wikipedia or other free sources online. Pages: 107. Chapters: Markov chain, Stochastic process, Statistical model, Power law, Linear model, Bayesian network, Mixture model, Item response theory, Discrete choice, Random walk, Predictive analytics, Errors-in-variables models, Rasch model, Rubin causal model, Generalized linear model, Restricted randomization, Directional statistics, Mediation, Total least squares, Cumulative frequency analysis, Generalized additive model for location, scale and shape, Common-cause and special-cause, Proportional hazards models, Independent component analysis, Mixed logit, Parametric model, Regression dilution, Classical test theory, Boolean analysis, Power transform, Segmented regression, Graphical model, Predictive modelling, Generalizability theory, Neighbourhood components analysis, Time-frequency representation, Population modeling, Regression model validation, Completely randomized design, Random effects model, Econometric model, Multilevel model, Generalized estimating equation, Independent and identically distributed random variables, Latent variable, Model selection, Stationary subspace analysis, Threshold model, Local independence, Rasch model estimation, Semiparametric model, Statistical interference, First-hitting-time model, Generative model, Moderation, Dummy variable, Latent variable model, Log-linear modeling, Rare disease assumption, Hierarchical linear modeling, Function approximation, Observational equivalence, Doubly stochastic model, Latent growth modeling, Exponential dispersion model, Marginal model, Epitome, Continuum structure function, Reification, Multinomial probit. Excerpt: A Markov chain, named for Andrey Markov, is a mathematical system that undergoes transitions from one state to another (from a finite or countable number of possible states) in a chainlike manner. It is a random process characterized as memoryless...
