Bayesian Inference
BayesDAG: Gradient-Based Posterior Inference for Causal Discovery
In this work, we introduce a scalable Bayesian causal discovery framework based on a combination of stochastic gradient Markov Chain Monte Carlo (SG-MCMC) and V ariational Inference (VI) that overcomes these limitations.
FunctionalEnsembleDistillation
One popular approach to alleviate this problem is using a Monte-Carlo estimation with an ensemble of models sampled from the posterior. However, this approach still comes at a significant computational cost, as one needs to store and run multiple models at testtime.
The Surprising Effectiveness of SP Voting with Partial Preferences
The SP algorithm requires the voters to predict other voters' report in the form of a full probability distribution over all rankings of alternatives.
Generalised Bayesian Filtering via Sequential Monte Carlo
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.
Calibrating Neural Simulation-Based Inference with Differentiable Coverage Probability
Bayesian inference allows expressing the uncertainty of posterior belief under a probabilistic model given prior information and the likelihood of the evidence. Predominantly, the likelihood function is only implicitly established by a simulator posing the need for simulation-based inference (SBI).