INSTRUCTOR: Dr. Fabrizio Ruggeri

 

COURSE MATERIAL AVAILABLE AT: link

 

CLASS SCHEDULE:

At the Room 2132 in Building 9, Saturday and Wednesday from 15:00PM to 16:30PM and Monday from 10:30AM to 12:00PM. Starts on Monday March, 25th and ends on Wednesday April, 24th

 

 

COURSE OBJECTIVES:

The student will be introduced to Bayesian modeling in selected, but relevant, stochastic processes and their applications: Markov chains, Poisson processes, reliability and queues. The use of real examples will be helpful in understanding why and how perform a Bayesian analysis. Students will be asked to analyze real data, from the elicitation of priors and modeling to (numerical) computation of estimates and forecasts and interpretation of findings.

 

 

COURSE OUTLINE:

• Introduction to Bayesian Analysis 
• Inference and prediction for discrete time and continuous time Markov chains
• Inference for Homogeneous and Nonhomogeneous Poisson processes
• Inference for stochastic models for repairable and non-repairable systems
• Inference and decision problems in queueing systems (M/M/1 queues, non-Markovian systems)