ST 474

Monte Carlo Methods

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Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).

Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).

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Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).

ST 474

Monte Carlo Methods

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Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).

Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).

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Simulating random numbers from various probability distributions; transformations of uniform variates; sampling from multivariate distributions; simulation of stochastic processes; (quasi-)Monte Carlo methods; variance reduction techniques. Applications may include: numerical integration of multivariate functions in high dimensions; approximation algorithms for solving matrix equations, partial differential equations and integral equations; pricing financial securities; MCMC methods; resampling techniques and other topics of computational statistics. Prerequisites: CP104 or MA207; MA200 or MA201; ST260 or (ST259 and one of ST230, ST231); and a 0.5 MA/ST credit at the 300 level (MA307 is recommended).