Maximum Likelihood Parameter Estimation for Latent Variable Models Using Sequential Monte Carlo. - Adam M. Johansen; Arnaud Doucet; Manuel Davy (ICASSP (3), 2006)

Particle methods for maximum likelihood estimation in latent variable models. - Adam M. Johansen; Arnaud Doucet; Manuel Davy (Stat. Comput., 2008)

Markov Chains. - Adam M. Johansen (Wiley Encyclopedia of Computer Science and Engineering, 2008)

On solving integral equations using Markov chain Monte Carlo methods. - Arnaud Doucet; Adam M. Johansen; Vladislav Z. B. Tadic (Appl. Math. Comput., 2010)

Bayesian model comparison via path-sampling sequential Monte Carlo. - Yan Zhou; Adam M. Johansen; John A. D. Aston (SSP, 2012)

Convergence of the k-Means Minimization Problem using Γ-Convergence. - Matthew Thorpe; Florian Theil; Adam M. Johansen; Neil Cade (SIAM J. Appl. Math., 2015)

Bayesian model comparison with un-normalised likelihoods. - Richard G. Everitt; Adam M. Johansen; Ellen Rowing; Melina Evdemon-Hogan (Stat. Comput., 2017)

Generalised Bayesian Filtering via Sequential Monte Carlo. - Ayman Boustati; Ömer Deniz Akyildiz; Theodoros Damoulas; Adam M. Johansen (NeurIPS, 2020)

Generalized Bayesian Filtering via Sequential Monte Carlo. - Ayman Boustati; Ömer Deniz Akyildiz; Theodoros Damoulas; Adam M. Johansen (CoRR, 2020)

Global Consensus Monte Carlo. - Lewis J. Rendell; Adam M. Johansen; Anthony Lee; Nick Whiteley (J. Comput. Graph. Stat., 2021)

A spatio-temporal model to reveal oscillator phenotypes in molecular clocks: Parameter estimation elucidates circadian gene transcription dynamics in single-cells. - Måns Unosson; Marco Brancaccio; Michael Hastings; Adam M. Johansen; Bärbel Finkenstädt (PLoS Comput. Biol., 2021)

Scalable particle-based alternatives to EM. - Juan Kuntz; Adam M. Johansen (CoRR, 2022)

Solving Fredholm Integral Equations of the First Kind via Wasserstein Gradient Flows. - Francesca R. Crucinio; Valentin De Bortoli; Arnaud Doucet; Adam M. Johansen (CoRR, 2022)

Product-form estimators: exploiting independence to scale up Monte Carlo. - Juan Kuntz; Francesca R. Crucinio; Adam M. Johansen (Stat. Comput., 2022)

The node-wise Pseudo-marginal method: model selection with spatial dependence on latent graphs. - Denishrouf Thesingarajah; Adam M. Johansen (Stat. Comput., 2022)

Particle algorithms for maximum likelihood training of latent variable models. - Juan Kuntz; Jen Ning Lim; Adam M. Johansen (AISTATS, 2023)

Divide-and-Conquer Fusion. - Ryan S. Y. Chan; Murray Pollock; Adam M. Johansen; Gareth O. Roberts (J. Mach. Learn. Res., 2023)

Cost free hyper-parameter selection/averaging for Bayesian inverse problems with vanilla and Rao-Blackwellized SMC samplers. - Alessandro Viani; Adam M. Johansen; Alberto Sorrentino (Stat. Comput., 2023)

Momentum Particle Maximum Likelihood. - Jen Ning Lim; Juan Kuntz; Samuel Power; Adam M. Johansen (ICML, 2024)

Particle Semi-Implicit Variational Inference. - Jen Ning Lim; Adam M. Johansen (NeurIPS, 2024)

Error bounds for particle gradient descent, and extensions of the log-Sobolev and Talagrand inequalities. - Rocco Caprio; Juan Kuntz; Samuel Power; Adam M. Johansen (CoRR, 2024)

Fast convergence of the Expectation Maximization algorithm under a logarithmic Sobolev inequality. - Rocco Caprio; Adam M. Johansen (CoRR, 2024)

Solving Fredholm Integral Equations of the Second Kind via Wasserstein Gradient Flows. - Francesca R. Crucinio; Adam M. Johansen (CoRR, 2024)

Redesigning the ensemble Kalman filter with a dedicated model of epistemic uncertainty. - Chatchuea Kimchaiwong; Jeremie Houssineau; Adam M. Johansen (CoRR, 2024)