7 papers
HyperMC: Multi-Fidelity Hyperparameter Tuning for Stochastic Gradient MCMC
Ming Tan, Xiyun Jiao
Stochastic gradient Markov chain Monte Carlo (SGMCMC) methods enable scalable Bayesian inference, but their performance depends strongly on hyperparameters such as the step size, m…
Structured Dimension-Matched Joint Variational Transdimensional Inference
Pingping Yin, Xiyun Jiao
Bayesian model selection couples a discrete model indicator with a model-specific continuous parameter space. We introduce structured dimension-matched variational transdimensional…
Mixing efficiency of trans-model Markov chain Monte Carlo algorithms with applications in Bayesian phylogenetics
Xiyun Jiao, Thomas Flouris, Ziheng Yang
Trans-model Markov chain Monte Carlo (MCMC) algorithms are widely used in Bayesian inference, and are particularly important in Bayesian phylogenetics where phylogenetic trees repr…
Using Variational Inference to Improve the Efficiency of MCMC Algorithms
Pingping Yin, Xiyun Jiao
Bayesian statistics makes inference based on Bayes' theorem, but the posterior distribution of unknown parameters is typically analytically intractable. To estimate the posterior,…
A Novel Framework Using Variational Inference with Normalizing Flows to Train Transport Reversible Jump Proposals
Pingping Yin, Xiyun Jiao
We propose a unified framework that employs variational inference (VI) with (conditional) normalizing flows (NFs) to train both between-model and within-model proposals for reversi…
Inference on Common Trends in a Cointegrated Nonlinear SVAR
James A. Duffy, Xiyu Jiao
We consider the problem of performing inference on the number of common stochastic trends when data is generated by a cointegrated CKSVAR (a two-regime, piecewise affine SVAR; Mavr…
Efficient Mirror-type Kernels for the Metropolis-Hastings Algorithm
Nuo Guan, Xiyun Jiao
We propose a new Metropolis-Hastings (MH) kernel by introducing the Mirror move into the Metropolis adjusted Langevin algorithm (MALA). This new kernel uses the strength of one ker…