arXiv subjects

Pingping Yin

Publications and source records attributed to Pingping Yin.

3 recordsLinked to original sources

arXiv2026

Structured Dimension-Matched Joint Variational Transdimensional Inference

Bayesian model selection couples a discrete model indicator with a model-specific continuous parameter space. We introduce structured dimension-matched variational transdimensional inference (SM-VTI) for finite enumerable model spaces. A rooted construction graph expresses a model as a sequence of local stop/child decisions. Each typed edge compiles a declared scientific parent-child edit into an exact native-coordinate dimension-matching lifting; an edge-conditioned flow then learns the residual continuous transport. The resulting local policy and conditional flow define one direct joint variational distribution, without embedding every model in a saturated maximum-dimensional surrogate. We derive its exact path density and optimize the joint reverse-KL objective. On a controlled 15-model target, SM-VTI-Joint recovers terminal masses, local actions, and nonlinear conditional geometry. On a 128-model misspecified robust variable-selection problem, a 10-data-set nearly parameter-matched affine comparison with AVTI shows stronger early model-mass recovery and competitive final joint accuracy under the same target-evaluation budget.

stat.CO↗

arXiv2026

Using Variational Inference to Improve the Efficiency of MCMC Algorithms

Bayesian statistics makes inference based on Bayes' theorem, but the posterior distribution of unknown parameters is typically analytically intractable. To estimate the posterior, two widely used numerical approximation methods are Markov Chain Monte Carlo (MCMC) and variational inference (VI). MCMC methods produce asymptotically exact samples but are computationally intensive, while VI methods are faster and more scalable but may lack accuracy. This paper proposes combining MCMC and VI to construct algorithms that leverage the strengths of both. The first proposed algorithm uses Gaussian variational inference (GVI) with various covariance structures to derive a linear transformation matrix for Hamiltonian Monte Carlo (HMC). This method improves the efficiency of HMC, particularly in high-dimensional and complex target distributions. The second algorithm combines a VI-based generative model, the variational auto-encoder (VAE), with the Metropolis-Hastings (MH) sampler. The resulting VAE-MH sampler is efficient and effectively traverses the parameter space, outperforming standard MCMC methods in identifying all modes of multi-modal distributions.

stat.CO↗

arXiv2025

A Novel Framework Using Variational Inference with Normalizing Flows to Train Transport Reversible Jump Proposals

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 reversible jump Markov chain Monte Carlo, enabling efficient trans-dimensional Bayesian inference. In contrast to the transport reversible jump (TRJ) of Davies et al. (2023), which optimizes forward KL divergence using pilot samples from the complex target distribution, our approach minimizes the reverse KL divergence, requiring only samples from a simple base distribution and largely reducing computational cost. Especially, we develop a novel trans-dimensional VI method with conditional NFs to fit the conditional transport proposal of Davies et al. (2023). We use RealNVP flows to learn the model-specific transport maps used for constructing proposals so that the calculation is parallelizable. Our framework also provides accurate estimates of marginal likelihoods, which may facilitate efficient model comparison and help design rejection-free proposals. Extensive numerical studies demonstrate that the TRJ method trained under our framework achieves faster mixing compared to existing baselines.

stat.ML↗