Markov models

Nonlinear least square optimization through parameter estimation using the unscented kalman filter in matlab

The following Matlab project contains the source code and Matlab examples used for nonlinear least square optimization through parameter estimation using the unscented kalman filter. The Kalman filter can be interpreted as a feedback approach to minimize the least equare error.

Hmm Matlab Code

A hidden Markov model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process with unobserved (hidden) states. A HMM can be presented as the simplest dynamic Bayesian network. The mathematics behind the HMM was developed by L. E. Baum and coworkers.[1][2][3][4][5] It is closely related to an earlier work on optimal nonlinear filtering problem by Ruslan L. Stratonovich,[6] who was the first to describe the forward-backward procedure.

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

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.

Monte carlo markov chain for inferring parameters for an ordinary differential equation model in matlab

The following Matlab project contains the source code and Matlab examples used for monte carlo markov chain for inferring parameters for an ordinary differential equation model. This function uses a Monte Carlo Markov Chain algorithm to infer parameters for an ordinary differential equation model of virus infection. This is a Bayesian non-linear mixed effects model.

Markov Random Field Matlab Code

Markov random field (often abbreviated as MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. A Markov random field is similar to a Bayesian network in its representation of dependencies; the differences being that Bayesian networks are directed and acyclic, whereas Markov networks are undirected and may be cyclic.

The following matlab project contains the source code and matlab examples used for markov random field.

Viterbi algorithm in matlab

The following Matlab project contains the source code and Matlab examples used for viterbi algorithm. This script calculates the most probable state sequence given a set of observations, transition probabilities between states, initial probabilities and observation probabilities.

Hidden markov modelling of contourlet transforms for art authentication in matlab

The following Matlab project contains the source code and Matlab examples used for hidden markov modelling of contourlet transforms for art authentication. Code used for the article "Authentication of paintings using hidden Markov modelling of contourlet transforms", where we develop a method for classification of paintings from digital reproductions.

Kalman Filter Matlab Code

Kalman filter is an algorithm that uses a series of measurements observed over time, containing noise (random variations) and other inaccuracies, and produces estimates of unknown variables that tend to be more precise than those based on a single measurement alone.

kalman filter

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