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Numerical Methods in Probability - lecture 6

Random walk approximation of Brownian motion. Tree methods. Computationally simple trees: the case of a constant diffusion and the general case. How to parallelize the algorithm.

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Numerical Methods in Probability - lecture 5

A general weak convergence result for diffusion processes. Monte Carlo simulation of a diffusion process: the Euler scheme. Discretization error and Monte Carlo error. How to parallelize the…

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Numerical Methods in Probability - lecture 4

Brownian motion. Nondifferentiability of paths. The stochastic integral and the Ito isometry. Ito processes and Ito formula. Stochastic differential equations.

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Numerical Methods in Probability - lecture 3

Markov chains: definition and examples. Simulation of a Markov chain. Simulation of Markov chains in the ergodic case.

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Numerical Methods in Probability - lecture 2

Variance reduction methods in Monte Carlo: antithetic variables, control variates, importance sampling. Examples and implementation

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Numerical Methods in Probability - lecture 1

Introduction to the course. Monte Carlo methods. Simulation of random variables. Error estimation with Monte Carlo methods.

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PhD course of Sergei Levendorskii - Lectures 7-8

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PhD course of Sergei Levendorskii - Lectures 3-4

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PhD course of Sergei Levendorskii - Lectures1-2 (end)

End of Lectures 1-2

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PhD course of Sergei Levendorskii - Lectures1-2

Lectures 1-2 (sudden fall of Internet connection at the end)

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