Department of Biomedical Engineering and Computational Science

LFM Toolbox for Matlab V1.0

Maintainers: Jouni Hartikainen, Simo Särkkä

News

  • 2012-06-20 First version of the toolbox.

Introduction

LFMT is a collection of routines that can be used to construct linear and non-linear latent force models (LFMs) in state-space form, and infer the state and parameters in these models with various Kalman filtering and smoothing type of methods. In a more general sense, the toolbox allows the user to construct models that are described with either linear or non-linear stochastic differential equations (SDEs) that are observed discretely in time. The purpose of the toolbox is not to provide highly optimized software package, but instead to provide a simple framework for building proof-of-concept implementations of SDE based models be used in practical applications.

Currently the toolbox is heavily under construction, so some bugs might still be looming around, particularly in demos, so be cautious.

Download

This software is distributed under the GNU General Public License (version 3 or later); please refer to the file License.txt, included with the software, for details.

LFM Toolbox V1.0

Make sure that you have included all subfolders inside the toolbox folder to your Matlab path.

Readme

See the following readme for brief user guide about the provided routines.

README.TXT

Features

Currently, the toolbox has the following features:

  • Linear SDEs:
    • Built-in models:
      • State-space Matern GP
      • 1st order LFM
      • 2nd order LFM
      • Stochastic resonator model
      • Wiener velocity model
      • Sum model combining any of the above
    • State inference: Kalman filter and smoother
    • Parameter inference:
      • MAP optimization (with gradients)
      • Adaptive MCMC (Robust Metropolis, RAM)
  • Non-linear SDEs:
    • Built-in models:
      • Transcription factor model (1st order non-linear LFM)
      • Ballistic reentry target model with range measurements
      • Ginzburg-Landau double well model
      • Forced Van der Pol oscillator model
    • State inference:
      • Continuous-discrete Gaussian filter and smoother (in both covariance and square-root forms with various numerical schemes for approximating the needed integrals)
      • Particle filter with stochastic Runge-Kutta
      • Grid filter and smoother numerically approximating the FPK
    • Parameter inference:
      • MAP optimization (without gradients)
      • Adaptive MCMC (Robust Metropolis, RAM)

Demos

There are several demonstration programs for the provided filters and smoothers. See the actual m-files for details and comments.

Contents

 LFM Toolbox for Matlab 
 Version 1.0, June 20. 2012

 Copyright (C) 2012 Jouni Hartikainen 
               2012 Simo Särkkä 
               
 History:      
   20.06.2012 Initial version

 This software is distributed under the GNU General Public
 Licence (version 2 or later); please refer to the file
 Licence.txt, included with the software, for details.
 

 Inference with LTI SDEs
   E_LTISDE   Marginal likelihood (ML) calculation for parameter inference
   DE_LTISDE  Derivative of ML w.r.t. given parameters 
   ES_LTISDE  Filtering and smoothing solutions 
   
 Inference with non-linear SDEs
   E_GF       ML with Gaussian filter
   ES_GF      Filtering and smoothing solutions with Gaussian filter
   E_SQRT_GF  ML with square root Gaussian filter
   ES_SQRT_GF Filtering and smoothing solutions with sqrt Gaussian filter
   E_SMC_SDE  ML with particle filter (returns also the particle approximations)
   E_PDE_SDE  ML with brute force filter solving the FPK in a grid
   DMP        Time derivatives of mean and covariance
   DMPC       Time derivatives of mean, covariance and cross-covariance
   DMA_SQRT   Time derivatives of mean and covariance (sqrt form)
   DMAC_SQRT  Time derivatives of mean, cov. and cross cov. (sqrt form)

 Utility functions
   LOGSUM     Log sum of logs
   MYODE      Wrapper function needed in ODE moment integration
   MA2VEC     Conversion functions used by sqrt non-linear filters
   MAC2VEC    ...
   VEC2MA     ...
   VEC2MAC    ...
   MP2VEC     Conversion functions used by non-linear filters
   MPC2VEC    ...
   VEC2MP     ...
   VEC2MPC    ...

 /EKFUKF/     Stripped-down version of EKF/UKF toolbox 

 /MISC/.      Miscellaneous functions 
     PRIOR_T    Student-t prior for parameters (taken from GPStuff package)
     SRKS15V    Stochastic Runge-Kutta (strong order 1.5) for solving SDEs

 /MODELS/            Built-in LTI SDE model functions
     GAUSSNCOV       Covariance matrix of Gaussian measurement noise
     LFM1_MODEL      1st order Latent Force Model
     LFM2_MODEL      2nd order Latent Force Model
     MATERN_MODEL    Matern GP model
     RESONATOR_MODEL Stochastic resonator model
     SUM_MODEL       Model forming the signal as sums any LTI SDEs
     WVEL_MODEL      Wiener velocity model


 /DEMOS/                 Demonstrations

   /BALLISTIC/
      BAllISTIC_DEMO     Demo of tracking a ballistic target on reentry
 
   /GINZBURG-LANDAU      Demo of Ginzburg-Landau double well model
    
   /GPR/             
      GPR_DEMO           Demo of LTI SDE inference
      GPR_SUM_DEMO       Demo of LTI SDE inference with sum model
 
   /LLFM/
      LFM1_DEMO          Demo of 1st order linear LFM 
      LFM2_DEMO          Demo of 2nd order linear LFM
  
   /TF/
      TF_DEMO            Demo of 1st order non-linear LFM (TF model)

   /VAN-DER-POL/
      VDP_DEMO           Demo of Van der Pol oscillator model


See also