# Ornstein uhlenbeck simulations and descretisation error in matlab

The following Matlab project contains the source code and Matlab examples used for ornstein uhlenbeck simulations and descretisation error.
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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.

The following Matlab project contains the source code and Matlab examples used for exact negative log likelihood of arma models via kalman filtering.
Several functions for evaluating the exact negative log-likelihood of ARMA models in O(n) time using the Kalman Filter.

The following Matlab project contains the source code and Matlab examples used for second generation vold kalman order filtering.
The Vold-Kalman Filter, introduced by Håvard Vold and Jan Leuridan in 1993, is able to extract non-stationary periodic components from a signal using a known frequency vector [1].

The following Matlab project contains the source code and Matlab examples used for hierarchical kalman filter for clinical time series prediction.
It is an implementation of hierarchical (a.

The following Matlab project contains the source code and Matlab examples used for neural network training using the unscented kalman filter.
Similar to using the extended Kalman filter, Neural Networks can also be trained through parameter estimation using the unscented Kalman filter.

The following Matlab project contains the source code and Matlab examples used for neural network training using the extended kalman filter.
The extended Kalman filter can not only estimate states of nonlinear dynamic systems from noisy measurements but also can be used to estimate parameters of a nonlinear system.

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.

The following Matlab project contains the source code and Matlab examples used for unconstrained optimization using the extended kalman filter.
The Kalman filter is actually a feedback approach to minimize the estimation error in terms of sum of square.

The following Matlab project contains the source code and Matlab examples used for dual extended kalman filter (dekf).
The package implements Dual Extended Kalman Filter function for the application of time-varying MVAR parameter estimation.

The following Matlab project contains the source code and Matlab examples used for kalman filter training for netlab.
Add-on for popular machine learning library 'NetLab' by Ian T. Nabney. Library implements Kalman Filter Training algorithms for NetLab.

The following Matlab project contains the source code and Matlab examples used for ud factorization & kalman filtering.
Description:
To enhance the efficiency and accuracy of Kalman filter computations, in particular the time and measurement updates, UD factorization is employed.

The following Matlab project contains the source code and Matlab examples used for extended kalman filter (ekf).
The state space model is nonlinear and is input to the function along with the current measurement. The function performs the extended Kalman filter update and returns the estimated next state and error covariance

The following Matlab project contains the source code and Matlab examples used for kalman filter application.
Corresponds to the paper "estimating and testing exponential-affine term structure models by kalman filter" published by Review of Quantitative Finance and Accounting in 1999.

The following Matlab project contains the source code and Matlab examples used for kalman filter in matlab (tutorial).
1. Detailed Tutorial on Kalman Filtering Techniques in Matlab
2.Computes the Kalman gain and the stationary covariance matrix using the Kalman filter of a linear forward looking model.

The following Matlab project contains the source code and Matlab examples used for ensemble kalman filter.
The algorithm used in this code is referenced from the following:
S Gillijns et al "What Is the Ensemble Kalman Filter and How Well Does it Work?"
Proceedings of the 2006 American Control Conference, Minneapolis, Minnesota, USA, June 14-16, 2006, pp 4448-4453.

The following Matlab project contains the source code and Matlab examples used for kalman filtering framework.
Object-based framework for performing Kalman filtering for discrete time systems or continuous-discrete hybrid systems.

The following Matlab project contains the source code and Matlab examples used for kalman filter.
This function performs Kalman filtering on data consisting of two
variables. A constant-velocity model is assumed. The filtered output and the error from the ground truth is computed.
Comments are welcome.

The following package and source code is Bayesian inference with stochastic volatility models using continuous superpositions of non-Gaussian Ornstein-Uhlenbeck processes (with M. F. J. Steel), Computational Statistics and Data Analysis, 54, 2594-2608.