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SIMULATION AND ESTIMATION OF STOCHASTIC DIFFERENTIAL EQUATIONS WITH MATLAB

SDE TOOLBOX - SIMULATION AND ESTIMATION OF STOCHASTIC DIFFERENTIAL EQUATIONS WITH MATLAB

"SDE Toolbox" is a MATLAB package for simulating sample paths of the solution of a Ito or Stratonovich stochastic differential equation (SDE), estimate 
parameters from data and visualize statistics. Users may simulate an SDE chosen from the provided models-library or implement their own one.
Run SDE_demo.m for a quick tour and read the user's guide (manual.pdf) for a detailed description of the package.

Project web-page: http://sdetoolbox.sourceforge.net

Copyright (C) 2007, Umberto Picchini
[e-mail]
http://www.biomatematica.it/pages/picchini.html



DOWNLOAD
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Available at http://sdetoolbox.sourceforge.net


REQUIREMENTS
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MATLAB 6.x or above. Operative systems: any MATLAB supported platform.
This version of the SDE Toolbox has been tested with Matlab 6.5 and 7.3.


GENERAL INFORMATIONS, INSTALLATION AND TUTORIALS
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Read manual.pdf in the SDE Toolbox folder or browse the following
http://sdetoolbox.sourceforge.net/manual.pdf

Further informations are available at http://sdetoolbox.sourceforge.net 


SDE Toolbox contains the following files
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CHANGES                                   lists the SDE Toolbox hystory of changes
demo_sdefile.m                            SDE model definition for SDE_demo.m
fminsearchbnd.m                           Nelder-Mead simplex (by John D'Errico, [e-mail])
M1_sdefile.m                              SDE definition for model M1 
M2_sdefile.m                              SDE definition for model M2            
M3_sdefile.m                              SDE definition for model M3 
M4_sdefile.m                              SDE definition for model M4 
M5_sdefile.m                              SDE definition for model M5  
M6_sdefile.m                              SDE definition for model M6 
M7_sdefile.m                              SDE definition for model M7 
M8_sdefile.m                              SDE definition for model M8 
M9_sdefile.m                              SDE definition for model M9 
M10_sdefile.m                             SDE definition for model M10 
LICENSE                                   the license file
manual.pdf                                the user's guide
perctile.m                                a percentile calculator (by Peter J. Acklam, [e-mail])
README                                    this file
SDE_compute_hessian.m                     Computes central approximation of Hessian free parameters with check on parameter bounds (originally written by Andrea De Gaetano, www.biomatematica.it)
SDE_demo.m                                a demonstration file
SDE_demo_interactive_settings.m           Asks the user to provide interactively the simulation settings for SDE_demo.m 
SDE_euler.m                               SDE numerical integration with the Euler-Maruyama method
SDE_euler_demo.m                          SDE numerical integration with the Euler-Maruyama method, created ad hoc for the SDE_demo.m 
SDE_getdata.m                             Load data from ASCII tab-delimited files
SDE_graph.m                               Plots the data (if available), empirical mean, confidence intervals, quartiles and histograms from the SDE solution process
SDE_integrator.m                          SDE numerical integration  
SDE_kurtosis.m                            Computes the sample kurtosis
SDE_library_optsetup.m                    Setup for the optimization procedure
SDE_library_run.m                         the model-library main routine
SDE_library_setup.m                       Library setup: among other things define the number of the state variables and the parameters for the models of the library
SDE_makelabel.m                           See the description section into SDE_makelabel.m
SDE_milstein.m                            SDE numerical integration with the Milstein method
SDE_milstein_demo.m                       SDE numerical integration with the Milstein method, created ad hoc for the SDE_demo.m and SDE_demo2.m
SDE_model_description.m                   Gives the description of the chosen SDE model
SDE_moment.m                              Computes sample moments
SDE_NPSML.m                               A non-parametric parameter estimation procedure for Ito and Stratonovich SDEs
SDE_NPSML_euler.m                         Euler-Maruyama integration to be used with SDE_NPSML.m 
SDE_NPSML_milstein.m                      Milstein integration to be used with SDE_NPSML.m 
SDE_param_mask.m                          Returns the vector of free parameters (originally written by Andrea De Gaetano, www.biomatematica.it)
SDE_param_unmask.m                        Returns the complete (free + constant) parameter set (originally written by by Andrea De Gaetano, www.biomatematica.it)
SDE_parcheck.m                            Checks that the free parameters are acceptable (originally written by by Andrea De Gaetano, www.biomatematica.it)
SDE_ParConfInt.m                          Computes the asymptotic 95% confidence intervals for the approximated parameters MLE 
SDE_predict.m                             Linear interpolation of simulated trajectories: useful to generate data
SDE_PSML.m                                Parametric Simulated Maximum Likelihood for Ito SDEs
SDE_PSML_euler.m                          Transition density approximation using the Euler-Maruyama integration (to be used with SDE_PSML.m)
sampledata1.dat                           An ASCII tab delimited file containing data generated from a one-dimensional SDE (read manual.pdf for details)
sampledata2.dat                           An ASCII tab delimited file containing data generated from a two-dimensional SDE (read manual.pdf for details)
sampledata3.dat                           An ASCII tab delimited file containing data generated from a one-dimensional SDE (read manual.pdf for details)
SDE_skewness.m                            Computes the sample skewness
SDE_split_sdeinput.m                      Split the sdefile input state-variables into separate slots
SDE_stats.m                               Computes descriptive statistics at the process end-time
SDE_xobsmatrix.m                          See the description section into SDE_xobsmatrix.m