A0279
Title: A Stein gradient descent approach for doubly intractable distributions
Authors: Jaewoo Park - Yonsei University (Korea, South) [presenting]
Abstract: Bayesian inference for doubly intractable distributions is challenging because they include intractable terms, which are functions of parameters of interest. Although several alternatives have been developed for such models, they are computationally intensive due to repeated auxiliary variable simulations. A novel Monte Carlo Stein variational gradient descent (MC-SVGD) approach is proposed for inference for doubly intractable distributions. Through an efficient gradient approximation, the MC-SVGD approach rapidly transforms an arbitrary reference distribution to approximate the posterior distribution of interest, without necessitating any predefined variational distribution class for the posterior. Such a transport map is obtained by minimizing Kullback-Leibler divergence between the transformed and posterior distributions in a reproducing kernel Hilbert space (RKHS). The convergence rate of the proposed method is also investigated. The application of the method is illustrated to challenging examples, including a Potts model, an exponential random graph model, and a Conway-Maxwell-Poisson regression model. The proposed method achieves substantial computational gains over existing algorithms, while providing comparable inferential performance for the posterior distributions.
