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Copy file name to clipboardExpand all lines: courses/stat544/syllabus.tex
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\subsubsection*{Course description}
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Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.
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Specification of probability models; subjective, conjugate, and noninformative prior distributions; hierarchical models; analytical and computational techniques for obtaining posterior distributions; model checking, model selection, diagnostics; comparison of Bayesian and traditional methods.
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\subsubsection*{Course objectives}
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To be able to
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To be able to
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\begin{itemize}
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\item Explain the basics of a Bayesian analysis including prior, likelihood, and posterior.
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\item Explain the basics of a Bayesian analysis including prior, likelihood, and posterior.
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\item Derive a conjugate Bayesian analysis with Jeffreys prior.
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\item Implement a computational Bayesian analysis using JAGS or Stan.
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\end{itemize}
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\subsubsection*{Assessment}
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\begin{itemize}
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\item Homework ($\sim$ 10): 20\%,
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\item Midterm (Mar 7): 40\%,
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\item Project:
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\item Homework ($\sim$ 10): 20\%,
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\item Midterm (Mar 5): 40\%,
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\item Project:
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\begin{itemize}
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\item Proposal (due Mar 22): 5\%
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\item Data description (due Apr 05): 5\%
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\subsubsection*{Reading schedule}
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The table below provides a reading schedule for the semester from
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The table below provides a reading schedule for the semester from
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Bayesian Data Analysis (3rd edition).
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`M' indicates the midterm and $\vert\vert$ indicates spring break.
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`M' indicates the midterm and $\vert\vert$ indicates spring break.
\href{https://www.celt.iastate.edu/instructional-strategies/preparing-to-teach/how-to-create-an-effective-syllabus/recommended-iowa-state-university-syllabus-statements/}{Center for Excellence in Teaching and Learning recommended syllabi statements}
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