Scalable Inference for Hybrid Bayesian Hidden MarkovModel Using Gaussian Process Emission

We provide the implementation and experiment results for the paper Scalable Inference for Hybrid Bayesian Hidden MarkovModel Using Gaussian Process Emission.

Description

Methods

• models/hmm_models_v4.py : approximate inference method for HMMGPSM (SVI + AGPE)
• models/emission_gp.py : exact gp emission
• models/emission_gp_rrff.py : approximate gp emission

Examples

• Section4.2-SVI.ipynb : HMMGPSM trained by SVI with the described synthetic dataset
• Section4.3-SVI+AGPE.ipynb : HMMGPSM trained by SVI+AGPE with the described synthetic dataset

Experiments

• experiments/main_exp1-1.py : Section 4.2 experiment with large T
• experiments/main_exp1-2.py : Section 4.3 experiment with large Nt
• experiments/main_exp2.py : Section 5.1 experiment with large T
• experiments/main_exp3.py : Section 5.2 experiment with large Nt

Requirements

• python >= 3.6
• torch >= 1.7
• pandas
• scikit-learn
• scipy
• munkres

Dataset

• datasets/synthetic/Q6_Fs200.mat/ : synthetic datset used for Section 4.2 experiment
• datasets/synthetic/Q6_Fs1000.mat/ : synthetic datset used for Section 4.3 experiment
• datasets/real/PigCVP10_Set_Dynamic_Downsample_rate1.mat/ : processed pigcvp dataset used for Section 5.1 experiment
• datasets/real/cwru_v1.pickle/ : processed CWRU dataset used for Section 5.2 experiment

Installation

git clone https://github.com/becre2021/abinferhmmgp
if necessary, install the required module as follows
pip3 install module-name
ex) pip3 install numpy 

Reference

• https://github.com/dillonalaird/pysvihmm
• https://github.com/lindermanlab/ssm
• http://www.tsc.uc3m.es/~miguel/downloads.php