Monte Carlo methods are a diverse class of algorithms that rely on repeated random sampling to compute the solution to problems whose solution space is too large to explore systematically or whose systemic behavior is too complex to model. This course introduces important principles of Monte Carlo techniques and demonstrates the power of these techniques with simple (but very useful) applications. Starting from the basic ideas of Bayesian analysis and Markov chain Monte Carlo samplers, we move to more recent developments such as slice sampling, multi-grid Monte Carlo, Hamiltonian Monte Carlo, and multi-nested methods. We complete our investigation of Monte Carlo samplers with streaming methods such as particle filters/sequential Monte Carlo. Throughout the course we delve into related topics in stochastic optimization and inference such as genetic algorithms, simulated annealing, probabilistic Gaussian models, and Gaussian processes. Applications to Bayesian inference and machine learning are used throughout. The recorded lectures are from the Harvard Faculty of Arts and Sciences course Applied Mathematics 207.
Prerequisites: introductory statistics, multivariate calculus, basic linear algebra, and basic knowledge of a computer programming language (such as C, Python and/or Matlab). (4 credits)
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