Laplace approximations for Deep Learning.
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Updated
Dec 6, 2024 - Python
Laplace approximations for Deep Learning.
Bayes-Newton—A Gaussian process library in JAX, with a unifying view of approximate Bayesian inference as variants of Newton's method.
distributed, likelihood-free inference
Approximate inference for Markov Gaussian processes using iterated Kalman smoothing, in JAX
A variational inference method with accurate uncertainty estimation. It uses a new semi-implicit variational family built on neural networks and hierarchical distribution (ICML 2018).
Bayesian Learning and Neural Networks (jupyter book sources)
Generative Models
Likelihood-Free Inference for Julia.
Approximate Bayesian Computation algorithm based on simulated annealing
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