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Found 4 papers, 2 papers with code
Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities
We prove non-asymptotic error bounds for particle gradient descent (PGD)~(Kuntz et al., 2023), a recently introduced algorithm for maximum likelihood estimation of large latent variable models obtained by discretizing a gradient flow of the free energy.
Momentum Particle Maximum Likelihood
Maximum likelihood estimation (MLE) of latent variable models is often recast as an optimization problem over the extended space of parameters and probability distributions.
A State-Space Perspective on Modelling and Inference for Online Skill Rating
We summarise popular methods used for skill rating in competitive sports, along with their inferential paradigms and introduce new approaches based on sequential Monte Carlo and discrete hidden Markov models.
Accelerated Sampling on Discrete Spaces with Non-Reversible Markov Processes
We consider the task of MCMC sampling from a distribution defined on a discrete space.
Computation 65C05
