Here’s Michael Betancourt writing in 2015: Leveraging the coherent exploration of Hamiltonian flow, Hamiltonian Monte Carlo produces computationally efficient Monte Carlo estimators, even with respect to complex and high-dimensional target distributions. When confronted with data-intensive applications, however, the algorithm may be too expensive to implement, leaving us to consider the utility of approximations such as […] The post “The Fundamental Incompatibility of Scalable Hamiltonian Monte Carlo and Naive Data Subsampling”…