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Model Selection and Multimodel Inference

introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that;

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Model Selection and Multimodel Inference

introduces information theoretic approaches and focuses critical attention on a priori modeling and the selection of a good approximating model that;

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Bayesian Model Selection and Statistical Modeling

Along with many practical applications, Bayesian Model Selection and Statistical Modeling presents an array of Bayesian inference and model;

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Bayesian Model Selection and Statistical Modeling

different types of simulation-based Bayesian model selection criteria, including the numerical calculation of Bayes factors.;

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Maximum Entropy and Bayesian Methods

inference in the natural and social sciences, engineering and medicine. Practical applications include: parametric model fitting and model selection;

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Rationality in an Uncertain World

, whereas logic is the calculus of certain inference. This mismatch means that logic is not the appropriate model for human thought. Oaksford and;

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Rationality in an Uncertain World: Essays in the Cognitive Science of Human Understanding

, whereas logic is the calculus of certain inference. This mismatch means that logic is not the appropriate model for human thought. Oaksford and;

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Statistical Inference

treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new;

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Statistical Inference

treatment of parameter inference and model comparisons that can be used with simple diffuse prior specifications. This novel approach provides new;

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Soft Computing with NeuroFuzzy Systems

recurrent neuro-fuzzy systems for fault prognosis; and neuro-fuzzy model for QoS based selection of web service. Section 2 focuses on adaptive;

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Bayesian Inference for Probabilistic Risk Assessment

of MCMC convergence and Bayesian model checking. Bayesian Inference for Probabilistic Risk Assessment is aimed at scientists and engineers who;

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Fundamental Statistical Inference

modern approaches. The recent literature on the misuse of hypothesis testing and p-values for model selection is discussed, and emphasis is given;

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Computer Age Statistical Inference, Student Edition

bootstrap, random forests, neural networks, Markov Chain Monte Carlo, inference after model selection, and dozens more. The distinctly modern;

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Computer Age Statistical Inference

jackknife and bootstrap, random forests, neural networks, Markov chain Monte Carlo, inference after model selection, and dozens more. The distinctly;

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Linear and Generalized Linear Mixed Models and Their Applications

, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners;

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Linear and Generalized Linear Mixed Models and Their Applications

, mixed model selection, and jackknife method in the context of mixed models. The book is aimed at students, researchers and other practitioners;

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Case Studies and Causal Inference: An Integrative Framework

is applied to research design tasks such as case selection and process tracing. The book presents the basics, state-of-the-art and arguments for;

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Logical Abilities in Children: Volume 2: Logical Inference

solution of certain syllogistic reasoning problems. Testable predictions of the model are easily derived, and the available evidence supports the;

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Logical Abilities in Children: Volume 2: Logical Inference

solution of certain syllogistic reasoning problems. Testable predictions of the model are easily derived, and the available evidence supports the;

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Statistical Inference for Fractional Diffusion Processes

Stochastic processes are widely used for model building in the social, physical, engineering and life sciences as well as in financial;

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Statistics for Astrophysics

in their field. This covers the foundations of Bayesian inference, Markov chain Monte Carlo technique, model building, Approximate Bayesian;

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Inference in Argumentation

. The authors propose and illustrate a model for the analysis of the standpoint-argument connection, called Argumentum Model of Topics (AMT). A;

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Bayesian Econometrics

considered include an endogenous selection ordered probit model, a censored treatment-response model, equilibrium job search models and various other;

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Bayesian Econometrics

considered include an endogenous selection ordered probit model, a censored treatment-response model, equilibrium job search models and various other;

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Multiple Realities of International Mediation

and outcomes of international mediation, introduces a metatheoretical model, looks at case studies to test her theories, and discusses the;

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Antedependence Models for Longitudinal Data

likelihood estimation of parameters as well as likelihood ratio tests and penalized likelihood model selection criteria for the model's covariance;

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Nondeductive Inference

deductive models. In particular, inductive inference, predictive inference, statistical inference, and decision making are examined in some detail;

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