# Monte Carlo methods projects and source code

# Simple rejection sampling in matlab

# Asian option pricing using monte carlo control variate method in matlab

# Mcmc Matlab Code

Markov chain Monte Carlo (MCMC) methods (which include random walk Monte Carlo methods) are a class of algorithms for sampling from probability distributions based on constructing a Markov chain that has the desired distribution as its equilibrium distribution. The state of the chain after a large number of steps is then used as a sample of the desired distribution. The quality of the sample improves as a function of the number of steps.

The following matlab project contains the source code and matlab examples used for mcmc.

# Markov Chain Matlab Code

# Metropolis hastings in matlab

# Adaptive metropolis hastings and factor slice sampling in matlab

# Metropolis hastings in matlab

# Mcmc -- markov chain monte carlo tools in matlab

# Differential evolution monte carlo sampling in matlab

# Particle Filter Matlab Code

Particle filters or Sequential Monte Carlo (SMC) methods are a set of on-line posterior density estimation algorithms that estimate the posterior density of the state-space by directly implementing the Bayesian recursion equations.

The following matlab project contains the source code and matlab examples used for particle filter.

# Kld sampling for particle filters using kullback leibler distance in matlab

# Resampling methods for particle filtering in matlab

# Simulated Annealing Matlab Code

**Simulated annealing (SA)** is a generic probabilistic metaheuristic for the global optimization problem of locating a good approximation to the global optimum of a given function in a large search space. It is often used when the search space is discrete (e.g., all tours that visit a given set of cities).