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Recent advances in stochastic gradient techniques have made it possible toestimate posterior distributions from large datasets via Markov Chain MonteCarlo (MCMC). However, when the target posterior is multimodal, mixingperformance is often poor. This results in inadequate exploration of theposterior distribution. A framework is proposed to improve the samplingefficiency of stochastic gradient MCMC, based on Hamiltonian Monte Carlo. Ageneralized kinetic function is leveraged, delivering superior stationarymixing, especially for multimodal distributions. Techniques are also discussedto overcome the practical issues introduced by this generalization. It is shownthat the proposed approach is better at exploring complex multimodal posteriordistributions, as demonstrated on multiple applications and in comparison withother stochastic gradient MCMC methods.

Stochastic Gradient Monomial Gamma Sampler