Sequential Monte Carlo for inference in nonlinear state space models
This code was downloaded from < https://github.com/compops/lic-thesis > and contains the code used to produce the results in the Licentiate's thesis
J. Dahlin, Sequential Monte Carlo for inference in nonlinear state space models. Linköping Studies in Science and Technology. Thesis. No. 1652, April 2014.
The thesis is available at < http://www.johandahlin.com/files/theses/dahlin-licthesis.pdf >. Put all the code into the same folder with the data directory as an intact subdirectory and execute first the Python code and then the R code to generate the plots. Some results might differ slightly from the thesis due to random differences in the data or the algorithms.
The main programs are written in Python 2.7 and makes use of NumPy 1.7.1, SciPy 0.12.0, Matplotlib 1.2.1, Pandas 0.13.1 and GPy 0.4.9. Please have these packages installed, on Ubuntu they can be installed using
sudo pip install --upgrade *package-name*
The plotting is done in R 3.0.1 and does not require any special packages. If any are missing from our system, they can be installed by executing the command
install.packages("packagename")in the R console.
ch2-example-likelihoodtheory Recreates the plot in "Score and information matrix for the LGSS model" in Example 2.6 in Section 2.3.
ch3-example-earthquakefilteringsmoothing Recreates the plot in "State inference in the earthquake count model" in Example 3.2 in Section 3.3.2.
ch3-example-forgettingproperties Recreates the plots in "Mixing property in the LGSS model" in Example 3.5 in Section 3.4.1.
ch3-example-garchpathdegenercy Recreates the plots in "Path degeneracy in the GARCH(1,1) model" in Example 3.3 in Section 3.3.2.
ch3-example-hwsvfilteringsmoothing Recreates the plot in "State inference in the Hull-White SV model in Example 3.6 in Section 3.4.1.
ch3-example-hwsvllscoreinfo Recreates the plot in "Score and information matrix in the Hull-White SV model" in Example 3.7 in Section 3.4.2.
ch3-example-importancesampling-hwsv Recreates the plot in "IS for Bayesian parameter inference in the HWSV model" in Example 3.1 in Section 3.2.
ch3-example-llestimationCLT Recreates the plot in "Bias and varaiance of the log-likelihood estimate" in Example 3.4 in Section 3.3.4.
ch4-example-earthinference Recreates the plot in "GPO for ML inference in the earthquake count model" in Example 4.9 in Section 4.4.3.
ch4-example-garchinference Recreates the plot in "PMH0 for parameter inference in the GARCH(1,1) model" in Example 4.4 in Section 4.3. This code takes some time to run (in the order of hours).
ch4-example-gp-acqfunc Recreates the plot in "GPO using different acqusition rules" in Example 4.7 in Section 4.4.2.
ch4-example-gpo-garchinference Recreates the plot in "GPO for ML inference in the GARCH(1,1) model" in Example 4.8 in Section 4.4.3.
ch4-example-gp-priorrealisations Recreates the plot in "GP kernels" in Example 4.5 in Section 4.4.1.
ch4-example-gp-regression Recreates the plot in "GP regression" in Example 4.6 in Section 4.4.1.
ch4-example-lgssinference-mixing Recreates the plot in "Parameter inference in the LGSS model" in Example 4.1 in Section 4.2.
ch4-example-lgssinference Recreates the plot in "Parameter inference in the LGSS model" in Example 4.1 in Section 4.2.
ch4-example-lgss-inputdesign Recreates the plot in "Input design in the LGSS model using GPO" in Example 4.10 in Section 4.4.3.
pmh.py Defines the general class for the particle MH algorithm and helper functions for this.
smc.py Defines the general class for sequential Monte Carlo algorithm.
kf.py Defines the general class for Kalman filtering and smoothing algorithm.
gpohelpers.py Defines the acqusition rules for the GPO algorithm.
classes.py Defines the different system models and generates the data.
helpers.py Defines different helpers for the other functions.