AMS207, Spring 2014, Section 01: Textbook and Grading

  • Textbook:
    • Bayesian Data Analysis, Third Edition. A. Gelman, J. B. Carlin, H. S. Stern, D.B. Dunson, A. Vehtari and D. B. Rubin. Chapman and Hall/CRC.
  • Other recommended books:
    • Bayesian Ideas and Data Analysis, Ronald Christensen, Wesley Johnson, Adam Branscum, and Timothy Hanson, Chapman and Hall/CRC
    • Bayesian Computation with R, Jim Albert, Springer
    • Markov Chain Monte Carlo - Stochastic Simulation for Bayesian Inference, Second Edition, Dani Gamerman and Hedibert Lopes, Chapman and Hall/CRC
    • Bayesian Methods for Data Analysis, Third Edition, Brad Carlin and Thomas Louis, CRC Press
    • Monte Carlo Statistical Methods, Second Edition, Christian Robert and George Casella
  • Grading: There will be one midterm (05/08, 45%), and two quizzes (04/22, 25%; 06/05, 30%). Exams and quizzes will be based on the homework. They will usually have two parts: one to be taken in class and one to take home. The take home part will involve the analysis of a case study and/or the application of some methods taken from an article published in one of the leading statistical journals. You will have to turn in a pdf file obtained by using the latex template based on the the ASA class
  • Homework: There will be periodical homework which will not be graded. Homework will give a very close indication of the material that will be covered in exams and quizzes. Some of the homework will involve numerical exercises.
  • Office Hours:
    • Bruno Sanso: M, W: 11:30 - 12:30
    • Pedro Regueiro: M: 2:00 - 3:00; Th 3:00 - 4:00