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Model Based Inference in the Life Sciences: A Primer on Evidence

science philosophy based on multiple working hypotheses and statistical models to represent them. The text is written for people new to the;

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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Confidence Intervals in Generalized Regression Models

A Cohesive Approach to Regression Models Confidence Intervals in Generalized Regression Models introduces a unified representation-the;

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Statistical Inference for Diffusion Type Processes

Decision making in all spheres of activity involves uncertainty. If rational decisions have to be made, they have to be based on the past;

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Measuring Statistical Evidence Using Relative Belief

of recent work on developing a theory of statistical inference based on measuring statistical evidence. It shows that being explicit about how to;

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Measuring Statistical Evidence Using Relative Belief

measuring statistical evidence and develops a theory of inference based on this method. He also discusses how statisticians should choose 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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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Statistical Inference

alternatives, based on Bayesian analyses, to current frequentist nonparametric methods. The text concludes with new goodness-of-fit methods for;

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Inference to the Best Explanation

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of Inference to the Best;

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Inference to the Best Explanation

How do we go about weighing evidence, testing hypotheses, and making inferences? According to the model of Inference to the Best;

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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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Evidence, Inference and Enquiry

? While this question was hotly contested and no clear final consensus emerged, much was learned on the journey. This book, based on the closing;

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Logical Abilities in Children: Volume 2: Logical Inference

Originally published in 1974, the second volume of four (Logical Inference: Underlying Operations) provides a process-model for the;

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Logical Abilities in Children: Volume 2: Logical Inference

Originally published in 1974, the second volume of four (Logical Inference: Underlying Operations) provides a process-model for the;

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Semimartingales and their Statistical Inference

modeling. Until now, however, researchers have had no single reference that collected the research conducted on the asymptotic theory for;

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Semimartingales and Their Statistical Inference

modeling. Until now, however, researchers have had no single reference that collected the research conducted on the asymptotic theory for;

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Bayesian inference with INLA

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time;

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Bayesian inference with INLA

The integrated nested Laplace approximation (INLA) is a recent computational method that can fit Bayesian models in a fraction of the time;

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Model Selection and Multimodel Inference

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book;

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Model Selection and Multimodel Inference

A unique and comprehensive text on the philosophy of model-based data analysis and strategy for the analysis of empirical data. The book;

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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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Family Health Care Nursing

-based, clinical focus built on a firm foundation of theory and research. From health promotion to end of life, a new organizational structure;

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Using R for Data Analysis in Social Sciences

Statistical analysis is common in the social sciences, and among the more popular programs is R. This book provides a foundation for;

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Agent-based Models and Causal Inference

a one-stop reference on the issue of causal inference in agent-based computational models.;

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Bayesian Statistics for Beginners

suited to making initial assessments based on incomplete or imperfect information; as that information is gathered and disseminated, the Bayesian;

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Causation, Evidence, and Inference

In this book, Reiss argues in favor of a tight fit between evidence, concept and purpose in our causal investigations in the sciences;

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