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  • Book
    Ton J. Cleophas, Aeilko H. Zeinderman.
    Summary: The current textbook has been written as a help to medical / health professionals and students for the study of modern Bayesian statistics, where posterior and prior odds have been replaced with posterior and prior likelihood distributions. Why may likelihood distributions better than normal distributions estimate uncertainties of statistical test results? Nobody knows for sure, and the use of likelihood distributions instead of normal distributions for the purpose has only just begun, but already everybody is trying and using them. SPSS statistical software version 25 (2017) has started to provide a combined module entitled Bayesian Statistics including almost all of the modern Bayesian tests (Bayesian t-tests, analysis of variance (anova), linear regression, crosstabs etc.). Modern Bayesian statistics is based on biological likelihoods, and may better fit clinical data than traditional tests based normal distributions do. This is the first edition to systematically imply modern Bayesian statistics in traditional clinical data analysis. This edition also demonstrates that Markov Chain Monte Carlo procedures laid out as Bayesian tests provide more robust correlation coefficients than traditional tests do. It also shows that traditional path statistics are both textually and conceptionally like Bayes theorems, and that structural equations models computed from them are the basis of multistep regressions, as used with causal Bayesian networks.

    Contents:
    Preface
    General Introduction to Modern Bayesian Statistics
    Traditional Bayes: Diagnostic Tests, Genetic Research, Bayes and Drug Trials
    Bayesian Tests for One Sample Continuous Data
    Bayesian Tests for One Sample Binary Data
    Bayesian Paired T-Tests
    Bayesian Unpaired T-Tests
    Bayesian Regressions
    Bayesian Analysis of Variance (Anova)
    Bayesian Loglinear Regression
    Bayesian Poisson Rate Analysis
    Bayesian Pearson Correlations
    Bayesian Statistics: Markov Chain Monte Carlo Sampling
    Bayes and Causal Relationships
    Bayesian Network
    Index.
    Digital Access Springer 2018