Hamiltonian Annealed Importance Sampling (HAIS)
HAIS is a method for combining Hamiltonian Monte Carlo (HMC) and Annealed Importance Sampling (AIS), so that a single Hamiltonian trajectory can stretch over many AIS intermediate distributions. This greatly improves the efficiency of AIS in continuous state spaces. It is described in detail in the paper:
J Sohl-Dickstein, BJ Culpepper
Hamiltonian annealed importance sampling for partition function estimation
Redwood Technical Report (2011)
The code in this repository can be used for log likelihood estimation, partition function estimation, and importance weight estimation. It can also be used as a Hamiltonian Monte Carlo sampler. See HAIS_examples.m for usage examples.
- HAIS_examples.m demonstrates the capabilities of this code in a variety of scenarios.
- HAIS.m performs Hamiltonian Annealed Importance Sampling.
- HAIS_logL.m calculates the log likelihood of a model given data using HAIS.
- HAIS_logL_aux.m calculates the log likelihood of a model with hidden (auxiliary) variables given data using HAIS.