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    <SEQUENTIAL>
      <record key="001" att1="001" value="184624" att2="184624">001   184624</record>
      <field key="037" subkey="x">englisch</field>
      <field key="050" subkey="x">Buch</field>
      <field key="076" subkey="">Formalwissenschaft</field>
      <field key="079" subkey="y">http://books.google.at/books?id=ZRMJ-CebFm4C&amp;printsec=frontcover</field>
      <field key="079" subkey="z">Kruschke, John K., Doing Bayesian Data Analysis (Google Book Search, Limited Preview)</field>
      <field key="100" subkey="">Kruschke, John K.</field>
      <field key="103" subkey="">Department of Psychological and Brain Sciences, Indiana University, Bloomington, USA</field>
      <field key="331" subkey="">Doing Bayesian Data Analysis</field>
      <field key="335" subkey="">A Tutorial with R and BUGS</field>
      <field key="403" subkey="">1. Ed.</field>
      <field key="410" subkey="">Amsterdam, Boston, Heidelberg</field>
      <field key="412" subkey="">Academic Press, an Imprint of Elsevier</field>
      <field key="425" subkey="">2011</field>
      <field key="433" subkey="">xvii, 653 pp.</field>
      <field key="517" subkey="c">from the Table of Contents: This Book's Organization: Read Me First!; The Basics. Parameters, Probability, Bayes' Rule, and R:</field>
      <field key="Int" subkey="r">oduction. Models We Believe In; What Is This Stuff Called Probability?; Bayes' Rule; All the Fundamentals Applied to</field>
      <field key="Inf" subkey="e">rring a Binomial Proportion: Inferring a Binomial Proportion via Exact Mathematical Analysis; Inferring a Binomial Proportion</field>
      <field key="via" subkey="">Grid Approximation; Inferring a Binomial Proportion via the Metropolis Algorithm; Inferring Two Binomial Proportions via</field>
      <field key="Gib" subkey="b">s Sampling; Bernoulli Likelihood with Hierarchical Prior; Hierarchical Modeling and Model Comparison; Null Hypothesis</field>
      <field key="Sig" subkey="n">ificance Testing; Bayesian Approaches to Testing a Point ("Null") Hypothesis; Goals, Power, and Sample Size; Applied to the</field>
      <field key="Gen" subkey="e">ralized Linear Model: Overview of the Generalized Linear Model; Metric Predicted Variable on a Single Group; Metric Predicted</field>
      <field key="Var" subkey="i">able with One Metric Predictor; Metric Predicted Variable with Multiple Metric Predictors; Metric Predicted Variable with One</field>
      <field key="Nom" subkey="i">nal Predictor; Metric Predicted Variable with Multiple Nominal Predictors; Dichotomous Predicted Variable; Ordinal Predicted</field>
      <field key="Var" subkey="i">able; Contingency Table Analysis; Tools in the Trunk;</field>
      <field key="540" subkey="">978-0-12-381485-2</field>
      <field key="544" subkey="">20407-A</field>
      <field key="700" subkey="b">519</field>
      <field key="700" subkey="b">Probabilities and applied mathematics</field>
      <field key="710" subkey="">Bayesian statistical decision theory</field>
      <field key="710" subkey="">R (Computer program language)</field>
    </SEQUENTIAL>
  </section>