Library for Sequential Monte Carlo Algorithms

Currently supports:

- SMC Sampler with
    + Adaptive resampling
    + Stratified resampling
    + Multinomial resampling
    + Systematic resampling
- Conditional SMC with multinomial resampling
- Particle MCMC:
    - PMMH
    - PG

Installation (on OSX)

• Dependencies: cmake, gsl, boost
  • brew install cmake
  • brew install gsl
  • brew install boost
• Then,

git clone https://github.com/junseonghwan/spf.git
cd spf
mkdir build
cd build
cmake ..
make install

The last line will install libSPF.a to /usr/local/binand header files to/usr/local/include/spf. Specify prefix for installation: make DESTDIR=/home/username/ install', which will place the files into /home/username/usr/local/bin and `/home/username/usr/local/include/spf'.

Usage

Example usage can be found under tests/ directory with DiscreteHMM and SVModel. Additionally, tests/test.cpp contains example code.

Defining state space and parameters

State space refers to the type of the values taken by the latent states X(t). For DiscreteHMM, the state space is int and for SVModel, it is double. But it could be any user defined type. If there is a single continuous parameter, it can simply be a double. In most settings, there are multiple parameters; therefore, it is recommended for the user to create a class representing the parameters.

Proposal for SMC

First extend ProblemSpecification. Implement the functions and constructor as necessary.

• propose_initial: returns initial state and its log weight.
• propose_next: returns next state and its log weight, given the current state.
• num_iterations: returns the number of iterations for SMC. Note that the class should accept reference to observations through the constructor as the observations are neccessary for computing the log weight of the samples.

SMCOptions

Instantiate this class and set various options.

#include "smc_options.hpp"

SMCOptions smc_options;
smc_options.num_particles = 10000;
smc_options.ess_threshold = 1.0;
smc_options.main_seed = 532;
smc_options.resampling_seed = 8234532;
smc_options.track_population = true;
smc_options.init();

Note: init() function generates random object for the seed provided to the options. This is bit cumbersome and it may change over time.

Running SMC

Assuming that the parameters are known, we can infer the latent states using SMC. It offers features to get log marginal likelihood estimate in addition to retrieving the particle population at each iteration of the SMC.

DiscreteHMMParams
ProblemSpecification<int, lDiscreteHMMParams> *smc_model = new DiscreteHMM(num_latent_states, y);
SMC<int, DiscreteHMMParams> smc(smc_model, smc_options);
smc.run_smc(params);
double log_marginal = smc.get_log_marginal_likelihood();
ParticlePopluation<int> *pop0 = smc.get_population(0); // returns the particle population for X(0)

ParticlePopulation offers functions for getting the state values and log weights.

PMCMCOptions

To be completed.

Running PMCMC

To be completed.