Bayesian metaphysics for experimental data

Introductory course in data analysis
Bayesian metaphysics for experimental data

Author Ksenia N. Kyzyurova


This course is an introductory course to data analysis. The course examines foundations for statistical model building and Bayesian statistical inference from such a model. The course focuses on applications of this methodology to analysis and inference from physical experiments. The ultimate goal of the course is to develop a student's intuition towards construction of such an experiment whose result is feasible for subsequent analysis within a simple statistical framework.


MAS111 (Mathematics Core II)


Outline syllabus

Teaching methods



Full syllabus

  1. Random variable; Distribution.
  2. Statistical model: likelihood function; parametric specification of a model.
  3. Bayesian inference: prior distribution, posterior distribution, marginal distribution, predictive distribution, posterior predictive distribution.
  4. Implementation of the normal statistical model: proper and improper priors; discrete and continuous distributions; subjective inference and objective inference.
  5. Inference from a statistical model: closed-form inference; conjugate analysis of a statistical model.
  6. Numerical inference from a statistical model: Gibbs sampling.
  7. Bayesian inverse problem: numerical sampling.
  8. Computational implementation of data analysis in R: basic functions, distribution functions, elements of code testing, visualization of data analysis.

Reading list