25 citations · 29 across the 7 of their papers we have counts for
7 papers
WaST: a formalisation of the Wave model with associated statistical inference and applications
Grégoire Clarté
We propose a mathematical formalisation of the ``wave model'' originally developed in historical linguistics but with further applications in human sciences. This model assumes new…
Practical Equivariances via Relational Conditional Neural Processes
Daolang Huang, Manuel Haussmann, Ulpu Remes +5
Conditional Neural Processes (CNPs) are a class of metalearning models popular for combining the runtime efficiency of amortized inference with reliable uncertainty quantification.…
Fast post-process Bayesian inference with Variational Sparse Bayesian Quadrature
Chengkun Li, Grégoire Clarté, Martin Jørgensen +1
In applied Bayesian inference scenarios, users may have access to a large number of pre-existing model evaluations, for example from maximum-a-posteriori (MAP) optimization runs. H…
A Phylogenetic Model of the Evolution of Discrete Matrices for the Joint Inference of Lexical and Phonological Language Histories
Grégoire Clarté, Robin J. Ryder
We propose a model of the evolution of a matrix along a phylogenetic tree, in which transformations affect either entire rows or columns of the matrix. This represents the change o…
Lagged couplings diagnose Markov chain Monte Carlo phylogenetic inference
Luke J. Kelly, Robin J. Ryder, Grégoire Clarté
Phylogenetic inference is an intractable statistical problem on a complex space. Markov chain Monte Carlo methods are the primary tool for Bayesian phylogenetic inference but it is…
Collective Proposal Distributions for Nonlinear MCMC samplers: Mean-Field Theory and Fast Implementation
Grégoire Clarté, Antoine Diez, Jean Feydy
Over the last decades, various "non-linear" MCMC methods have arisen. While appealing for their convergence speed and efficiency, their practical implementation and theoretical stu…
Component-wise approximate Bayesian computation via Gibbs-like steps
Grégoire Clarté, Christian P. Robert, Robin Ryder +1
Approximate Bayesian computation methods are useful for generative models with intractable likelihoods. These methods are however sensitive to the dimension of the parameter space,…