no code implementations • 23 Dec 2022 • Oskar Kviman, Hazal Koptagel, Harald Melin, Jens Lagergren
Over the years, sequential Monte Carlo (SMC) and, equivalently, particle filter (PF) theory has gained substantial attention from researchers.
1 code implementation • 1 Mar 2022 • Hazal Koptagel, Oskar Kviman, Harald Melin, Negar Safinianaini, Jens Lagergren
The exponential size of the tree space is, unfortunately, a substantial obstacle for Bayesian phylogenetic inference using Markov chain Monte Carlo based methods since these rely on local operations.
1 code implementation • 22 Feb 2022 • Oskar Kviman, Harald Melin, Hazal Koptagel, Víctor Elvira, Jens Lagergren
In variational inference (VI), the marginal log-likelihood is estimated using the standard evidence lower bound (ELBO), or improved versions as the importance weighted ELBO (IWELBO).
no code implementations • 5 Sep 2015 • Kamer Kaya, Figen Öztoprak, Ş. İlker Birbil, A. Taylan Cemgil, Umut Şimşekli, Nurdan Kuru, Hazal Koptagel, M. Kaan Öztürk
We propose HAMSI (Hessian Approximated Multiple Subsets Iteration), which is a provably convergent, second order incremental algorithm for solving large-scale partially separable optimization problems.
no code implementations • 3 Jun 2015 • Umut Şimşekli, Hazal Koptagel, Hakan Güldaş, A. Taylan Cemgil, Figen Öztoprak, Ş. İlker Birbil
For large matrix factorisation problems, we develop a distributed Markov Chain Monte Carlo (MCMC) method based on stochastic gradient Langevin dynamics (SGLD) that we call Parallel SGLD (PSGLD).