Summary:
This is a 3-credit graduate-level course covering the following topics: interpretation of probability; common probability distributions; Bayesian inference; basic descriptive statistics; frequentist statistical inference; frequentist hypothesis testing; maximum entropy probabilities; linear model fitting; nonlinear model fitting; Markov chain Monte Carlo methods; methods of error propagation; systematic uncertainties; parameter estimation; hypothesis testing and statistical significance; robust statistics.
Prerequisites:
Officially, none. However, you will be expected to have some facility with computational techniques and programming in a high-level language, or at least a willingness to learn very quickly. Quite simply, it's not possible to do much data analysis or statistics without being able to program. Almost all homework assignments will have a substantive computational component, although this class will not teach programming per se. If you don't already know basic computational physics, your time might be better spent taking Physics 410 instead.
Lectures:
Tuesday, Thursday 2:00-3:15
Hennings 302
Office hours:
Wednesday 4:00-5:00 pm, Hennings 341.
Drop-ins welcome, or email me for a specific appointment.
Text book:
Bayesian Logical Data Analysis for the Physical Sciences, 1st edition, P. Gregory, ISBN:9780521150125.
Lecture slides:
Indexed by date
Homework assignments:
Indexed by assignment number
Grading information:
Homework (~6 problem sets): 65%, final exam (take home): 35%
Useful software:
This course will require some computational facility on your part. The entire course can be done using free software, and you're not required to buy anything. The most important things you'll need are access to a good plotting package and a library of scientific routines (capable of random number generation, non-linear fitting, and matrix operations at a minimum). I encourage you to use whatever tools your field uses or that you already know.