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Modelling with Stochastic Processes
Syllabus
Schedule
Lecture Notes
Lecture 1: Overview
Lecture 2: Forward sampling continued
Lecture 3: Bayesian statistics: from parametric to non-parametric
Lecture 4: Dirichlet process: inference, properties and extensions
Lecture 5: More on inference
Lecture 6: Topics in Bayesian non-parametrics
Lecture 7: Related topics in computational statistics
Lecture 8: Point processes and Levy measures
Lecture 9: Point and jump processes
Lecture 10: Jump processes: asymptotic behavior and applications
Lecture 11: Diffusions
Activities
Exercise 1: Forward simulation
Exercise 2: Exploring Bayesian models with JAGS
Exercise 3: Bayesian non-parametric density estimation
Exercise 4: CTMCs, factor graphs and phylogenetics
Labs
Lab 1: Forward simulation
Lab 2: Bugs and java basics
Lab 3: Bugs and java basics, continued
Lab 4: Implementing probabilistic inference software
Lab 5: Implementing probabilistic inference software continued
Lab 6: Implementing probabilistic inference software continued
Lab 7: BLang and phylogenetic inference
Contacts
Lab 7: BLang and phylogenetic inference
05 Mar 2014
Overview
In this lab, we will talk about the last exercise.