Course contents
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Monte Carlo Methods
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Sensitivity analysis
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Random number generation
Simulation methodology
Bootstrap
Markov Chain Monte Carlo
Screening methods
Variance-based methods
Numerical linear algebra
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Systems of linear equations
Optimization methods
Computational statistics, course
introduction
Random number generation
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Generating pseudo random numbers with a uniform
distribution on the unit interval (0,1)
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Generating random numbers with a given cumulative
distribution function F(x)
Computational statistics, course
introduction
Simulation methodology
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Crude Monte Carlo simulations
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Antithetic sampling
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Simulations using quasi random numbers
Computational statistics, course
introduction
The Bootstrap
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Substituting un unknown distribution function for an
empirical distribution function
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Resampling techniques
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Bootstrap intervals
Computational statistics, course
introduction
Markov Chain Monte Carlo
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Metropolis-Hastings algorithm
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Gibbs sampling
Computational statistics, course
introduction
Sensitivity analysis – screening methods
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One-at-time designs
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Fractional factorial designs
Computational statistics, course
introduction
Sensitivity analysis – variance-based methods
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Measures of variation
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Designs of computer experiments
Computational statistics, course
introduction
Systems of linear equations
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Choleski decomposition
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QR decomposition
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Singular-value decomposition
Computational statistics, course
introduction
Systems of linear equations

Choleski decomposition

QR decomposition

Singular-value decomposition
Computational statistics, course
introduction
Optimization

Steepest decent methods
Computational statistics, course
introduction