553SM - BAYESIAN STATISTICS 2024

Section outline

• Select section General

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  General

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  • Select activity Annunci

    

    Annunci Forum
• Select section Slides (Trevisani)

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  Slides (Trevisani)

  Some slides and video lectures from previous years (Pauli) are available for review.

  • Select activity General information

    

    General information File PDF
  • Select activity Introduction to Bayesian Statistics (part 1)

    

    Introduction to Bayesian Statistics (part 1) File PDF
  • Select activity Introduction to BS (part 2)

    

    Introduction to BS (part 2) File PDF
  • Select activity Introduction to BS (part 3)

    

    Introduction to BS (part 3) File PDF
  • Select activity Accessory material

    

    Accessory material Folder
  • Select activity Introduction

    

    Introduction Page
  • Select activity One-parameter models (general introduction, Binomial model)

    

    One-parameter models (general introduction, Binomial model) File PDF
  • Select activity Lab on binomial model

    

    Lab on binomial model File HTML
  • Select activity One-parameter models (Normal model, Other models, Prior choice))

    

    One-parameter models (Normal model, Other models, Prior choice)) File PDF
  • Select activity Multiparameter models

    

    Multiparameter models File PDF
  • Select activity Lab on Multiparameter models

    

    Lab on Multiparameter models File HTML
• Select section Slides (Egidi)

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  Slides (Egidi)

  • Select activity Generalized linear models

    

    Generalized linear models File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Hierarchical/multilevel models

    

    Hierarchical/multilevel models File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Laplace's approximation and importance sampling

    

    Laplace's approximation and importance sampling File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity MCMC methods

    

    MCMC methods File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Rhat and effective sample size: some notes

    

    Rhat and effective sample size: some notes File PDF
  • Select activity Hamiltonian Monte Carlo

    

    Hamiltonian Monte Carlo File PDF
  • Select activity EM algorithm and mixture models

    

    EM algorithm and mixture models File PDF
  • Select activity Variational inference methods

    

    Variational inference methods File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Model checking

    

    Model checking File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Model comparisons and information criteria

    

    Model comparisons and information criteria File PDF
  • Select activity R code and data

    

    R code and data File ZIP
  • Select activity Introduction to Stan (old material)

    

    Introduction to Stan (old material) File PDF
  • Select activity R code and data

    

    R code and data File ZIP
• Select section Books

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  Books

  • Select activity Bayesian Data Analysis (2nd ed.)

    

    Bayesian Data Analysis (2nd ed.) File PDF
  • Select activity Bayesian Data Analysis (3rd ed.)

    

    Bayesian Data Analysis (3rd ed.) File PDF
  • Select activity Bayesian Computation with R

    

    Bayesian Computation with R File PDF
  • Select activity Monte Carlo Statistical Methods

    

    Monte Carlo Statistical Methods File PDF
  • Select activity Introducing Monte Carlo Methods with R

    

    Introducing Monte Carlo Methods with R File PDF
  • Select activity Data Analysis using Regression and Multilevel/Hierarchical Models

    

    Data Analysis using Regression and Multilevel/Hierarchical Models File PDF
  • Select activity Stan Users Guide 2019

    

    Stan Users Guide 2019 File PDF
• Select section Exam

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  Exam

  • Select activity List of possible projects for the exam 2024/2025

    

    List of possible projects for the exam 2024/2025 File PDF
  • Select activity Instructions for the final exam

    Instructions for the final exam of Bayesian statistics. Check the official dates in the esse3 system and remind to register yourself. If you need another date other than the official one, please contact the professors by email.

    - Master students

    1. The final exam is written, consists of 3/4 open questions and focuses on the topics covered during the course. The total length does not exceed two hours, but it could also be less than two hours.
    2. The 'flavour' is not merely theoretical but also from a practitioner perspective. 
    3. For instance, the student could be asked to write some equations, do some small computations, and explain some formulas or algorithmic procedures. 
    4. Before doing the written exam, the student must choose, some weeks before, one among the practical projects above and send only the solved and compiled project in RMarkdown the professors via mail 2/3 days before the exam takes place: keep in mind that the student is free to choose the project and the type of presentation (html, slides, pdf, power point). 
    5. The .rmd presentation should be 'self-contained' and include any relevant data analysis (plots, tables, equations, etc.) 
    6. In case the student wants to propose his/her own data set, he/she is asked to contact the professors some weeks before the planned 'appello'.
    7. After the written exam, the professors will let the student know about the evaluation of the project and the written exam. Note that the written exam constitutes the 60% of the final evaluation.

    - PhD students 

    1. The final exam is oral. 
    2. To do the oral exam, the student must choose one among the practical projects above and send the solved project in RMarkdown the professors via mail 2/3 days before the exam takes place: keep in mind that the student is free to choose the project and the type of presentation (html, slides, pdf, power point). 
    3. The student is encouraged to propose to the professors his/her own data set some weeks before the planned 'appello'. 
    4. The presentation should be 'self-contained' and include any relevant data analysis (plots, tables, equations, etc.)
    5. The oral exam basically consists in a 20-minutes project presentation (slides, html, pdf, power point).

• Select section Papers

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  Papers

  • Select activity Gelman et al. 2008

    

    Gelman et al. 2008 File PDF
  • Select activity Gabry et al. 2019

    

    Gabry et al. 2019 File PDF
  • Select activity Langford et al. 1998

    

    Langford et al. 1998 File PDF
  • Select activity Vella & Verbeek 1998

    

    Vella & Verbeek 1998 File PDF
  • Select activity Baio & Blangiardo 2010

    

    Baio & Blangiardo 2010 File PDF
  • Select activity Vigotti et al. 1996

    

    Vigotti et al. 1996 File PDF
  • Select activity Blei et al. 2019

    

    Blei et al. 2019 File PDF
  • Select activity Betancourt 2016

    

    Betancourt 2016 File PDF
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