[HTML][HTML] PyMC: a modern, and comprehensive probabilistic programming framework in Python
PyMC is a probabilistic programming library for Python that provides tools for constructing
and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural …
and fitting Bayesian models. It offers an intuitive, readable syntax that is close to the natural …
Diffusion probabilistic modeling of protein backbones in 3d for the motif-scaffolding problem
Construction of a scaffold structure that supports a desired motif, conferring protein function,
shows promise for the design of vaccines and enzymes. But a general solution to this motif …
shows promise for the design of vaccines and enzymes. But a general solution to this motif …
Sequential monte carlo: A unified review
Sequential Monte Carlo methods—also known as particle filters—offer approximate
solutions to filtering problems for nonlinear state-space systems. These filtering problems …
solutions to filtering problems for nonlinear state-space systems. These filtering problems …
An invitation to sequential Monte Carlo samplers
ABSTRACT Statisticians often use Monte Carlo methods to approximate probability
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …
distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential …
Practical and asymptotically exact conditional sampling in diffusion models
Diffusion models have been successful on a range of conditional generation tasks including
molecular design and text-to-image generation. However, these achievements have …
molecular design and text-to-image generation. However, these achievements have …
Automatic differentiation of programs with discrete randomness
Automatic differentiation (AD), a technique for constructing new programs which compute
the derivative of an original program, has become ubiquitous throughout scientific …
the derivative of an original program, has become ubiquitous throughout scientific …
Differentiable particle filtering via entropy-regularized optimal transport
A Corenflos, J Thornton… - International …, 2021 - proceedings.mlr.press
Particle Filtering (PF) methods are an established class of procedures for performing
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …
inference in non-linear state-space models. Resampling is a key ingredient of PF necessary …
[图书][B] Distributional reinforcement learning
The first comprehensive guide to distributional reinforcement learning, providing a new
mathematical formalism for thinking about decisions from a probabilistic perspective …
mathematical formalism for thinking about decisions from a probabilistic perspective …
Path integral sampler: a stochastic control approach for sampling
We present Path Integral Sampler~(PIS), a novel algorithm to draw samples from
unnormalized probability density functions. The PIS is built on the Schr\" odinger bridge …
unnormalized probability density functions. The PIS is built on the Schr\" odinger bridge …