| Date | Topics |
| Week 1 (Jan.12) | Conditional probability and conditional expectations: review (3.1-3.3)
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| Week 2 (Jan.19) | Computing probabilities, expectations by conditioning. Markov chain basics: (2.8,3.4,3.5, 4.1)
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| Week 3 (Jan.26) | Gambler's ruin, Chapman-Kolmogorov Equations, Classification of states (4.2,4.3)
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| Week 4 (Feb.2) | Recurrence, Ergodic theorem, limiting probabilities (4.4, 4.5)
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| Week 5 (Feb.9) | Reversibility, Ehrenfest Model, Branching Processes (4.8)
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| Week 6 (Feb.16) | Poisson Processes (5.1-5.3)
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| Week 7 (Feb.23) | more on Poisson Processes. Continuous-time Markov chain basics.
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| Week 8 (Mar.2) | Continuous-time Markov chains, birth-death processes. Midterm exam on Wednesday, March 4th
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| March 9-13 | Spring break
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| Week 9 (Mar.16) | I.i.d. Monte Carlo: basic theory (handouts, 11.1, 11.2.1, 11.2.2)
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| Week 10 (Mar.23) | Rejection sampling, Importance sampling (handouts)
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| Week 11 (Mar.30) | Importance sampling
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| Week 12 (Apr.6) | Markov chain Monte Carlo (MCMC): basic theory. The Metropolis-Hastings/Gibbs samplers
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| Week 13 (Apr.13) | M-H variants, MCMC implementation issues
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| Week 14 (Apr.20) | More theory and implementation issues
Take home final out Wednesday, April 22nd, due Monday, April 27th
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| Week 15 (Apr.27) | MCMC for maximum likelihood estimation. Simulation studies.
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| Week 16 (May.4) | Final exam: TBA
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