Variational Mixtures and Multi-Marginal Flow Matching: Advancing Statistical Inference with Biological Applications

2026-09-29

AI Summary

This study addresses the challenge of statistical inference for multimodal geometric distributions in complex biological systems, particularly when density functions are defined only up to a normalizing constant. Building upon variational inference, this work integrates black-box variational inference, multiple importance sampling ELBO, and flow matching techniques to construct a highly expressive inference framework tailored for spatial transcriptomics. Key contributions include proposing CoLN, an unnormalized target density; revealing and refuting the conventional belief regarding performance gains of mixture models in variational inference; and developing a hybrid approach that combines multi-marginal flow matching with variational interpolation. Ultimately, this framework substantially enhances both the efficiency of approximate inference and the analytical capability for multimodal biological data, such as three-dimensional spatial transcriptomics.

Abstract

In this thesis I develop methods for statistical inference when the distributions arising from complex biological systems are multi-modal, geometrically structured, and sometimes only defined up to a normalizing constant. I start from variational inference and, when analytic update equations are unavailable, move to black-box variational inference. To build intuition regarding inference challenges and the proposed methodologies, I introduce a novel unnormalized target density (the CoLN distribution) and reuse it as a controlled test case in the kappa. I then trace a trajectory of increasingly expressive approximations: ensembles evaluated with the multiple importance sampling ELBO (Paper A) and variational mixtures that automate component cooperation and exploration (Paper B). Because expressivity comes at a cost, I develop efficient mixture learning ideas, including Monte Carlo objective estimators to scale mixture learning more efficiently (Paper C). As a new result in the kappa, I overturn a three decades long misconception regarding the potential performance benefits of using mixtures in variational inference. Finally, I move from variational inference to flow matching, where I address the need for specialized treatment of interpolant learning in multi-marginal settings (Paper D). By combining insights from Papers A-D, I derive in Section 5.5 a new method: multi-marginal flow matching with mixtures of variational interpolants. I connect these methodological developments to biological applications, with special emphasis on three-dimensional spatial transcriptomics, where stacked tissue slices induce multi-modal dynamics across space.

Problem

• statistical inference
• multi-modal distributions
• variational inference
• spatial transcriptomics
• unnormalized density

Innovation

• Variational Inference
• Mixture Models
• Flow Matching
• Multi-Marginal
• Spatial Transcriptomics