Bayesian Inference is a methodology that employs Bayes Rule to estimate parameters (and their full posterior).
High-dimensional Bayesian inference problems cast a long-standing challenge in generating samples, especially when the posterior has multiple modes.
As such, learning the laws is then reduced to symbolic regression and Bayesian inference methods are used to obtain the distribution of unobserved properties.
We report the computational efficiency and statistical reliability of our method in numerical experiments of the language modeling using RNNs, and the out-of-distribution detection with DNNs.
Bayesian inference over the reward presents an ideal solution to the ill-posed nature of the inverse reinforcement learning problem.
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.
To infer causal relationships in zero-inflated count data, we propose a new zero-inflated Poisson Bayesian network (ZIPBN) model.
Depending on the cases, the benefits can include an alleviation of the geometric pathologies that frustrate Hamiltonian Monte Carlo and a dramatic speed-up.
These models are specified as probabilistic programs, allowing us to represent and perform efficient Bayesian inference over an agent's goals and internal planning processes.