Bayesian Inference
Supplement for Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows A Subsplit Bayesian networks D AB C AB CD ABC D ABC D AB CD D AB CD AB CD AB CD assign
Figure 1: A simple subsplit Bayesian network for a leaf set that contains 4 species A, B, C and D. This figure is adapted from Zhang and Matsen IV (2019). SBN (the one with a full and complete binary tree structure as shown in Figure 1) is good enough. The SBN framework also generalizes to unrooted trees, which are the most common type of phylogenetic trees. (Zhang and Matsen IV, 2018). Sampling from SBNs is also straightforward via ancestral sampling.
Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows
We show that VBPI-NF significantly improves upon the vanilla VBPI on a benchmark of challenging real data Bayesian phylogenetic inference problems.
Multi-fidelity Monte Carlo: a pseudo-marginal approach
Prescott and Baker (2020) propose a multi-fidelity approach to ABC. In our work, the Russian roulette estimator is used to construct an unbiased, low-fidelity likelihood.
Across-animal odor decoding by probabilistic manifold alignment
When applied to recordings from the mouse olfactory bulb, our approach reveals low-dimensional population dynamics that are odor specific and have consistent structure across animals.
All your loss are belong to Bayes Christian Walder 1 2 Richard Nock
In this paper, we rely on a broader view of proper composite losses and a recent construct from information geometry, source functions, whose fitting alleviates constraints faced by canonical links.
Laplace Redux - Effortless Bayesian Deep Learning
Despite their successes, modern neural networks (NNs) still suffer from several shortcomings that limit their applicability in some settings.
