Multilevel Monte Carlo for Scalable Bayesian Computations

Understand

Markov chain Monte Carlo (MCMC) algorithms are ubiquitous in Bayesian computations.

• However, they need to access the full data set in order to evaluate the posterior density at every step of the algorithm.
• This results in a great computational burden in big data applications.
• In contrast to MCMC methods, Stochastic Gradient MCMC (SGMCMC) algorithms such as the Stochastic Gradient Langevin Dynamics (SGLD) only require access to a batch of the data set at every step.