no code implementations • 4 Mar 2024 • Rocco Caprio, Juan Kuntz, Samuel Power, Adam M. Johansen
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.
no code implementations • 12 Dec 2023 • Jen Ning Lim, Juan Kuntz, Samuel Power, Adam M. Johansen
By discretizing the system, we obtain a practical algorithm for MLE in latent variable models.
1 code implementation • 27 Apr 2022 • Juan Kuntz, Jen Ning Lim, Adam M. Johansen
(Neal and Hinton, 1998) recast maximum likelihood estimation of any given latent variable model as the minimization of a free energy functional $F$, and the EM algorithm as coordinate descent applied to $F$.
2 code implementations • 14 Oct 2021 • Ryan S. Y. Chan, Murray Pollock, Adam M. Johansen, Gareth O. Roberts
Many existing approaches resort to approximating the individual sub-posteriors for practical necessity, then find either an analytical approximation or sample approximation of the resulting (product-pooled) posterior.
no code implementations • 23 Feb 2020 • Ayman Boustati, Ömer Deniz Akyildiz, Theodoros Damoulas, Adam M. Johansen
We introduce a framework for inference in general state-space hidden Markov models (HMMs) under likelihood misspecification.
no code implementations • 1 Apr 2015 • Richard G. Everitt, Adam M. Johansen, Ellen Rowing, Melina Evdemon-Hogan
Models for which the likelihood function can be evaluated only up to a parameter-dependent unknown normalising constant, such as Markov random field models, are used widely in computer science, statistical physics, spatial statistics, and network analysis.
3 code implementations • 19 Jun 2014 • Fredrik Lindsten, Adam M. Johansen, Christian A. Naesseth, Bonnie Kirkpatrick, Thomas B. Schön, John Aston, Alexandre Bouchard-Côté
We propose a novel class of Sequential Monte Carlo (SMC) algorithms, appropriate for inference in probabilistic graphical models.