activity
20192026
collaborators

6 papers

stat.CO2026

Score-Based Martingale Posteriors for Deep Neural Networks

Abylay Zhumekenov, Ajay Jasra, Mohamed Maama +1

In this paper we investigate the efficacy of the score-based martingale posteriors (SMP) (Cui & Walker, 2025; Fong et al., 2023) in the context of modern and large-scale machine le…

stat.CO2026

Particle Filtering for a Class of State-Space Models with Low and Degenerate Observational Noise

Abylay Zhumekenov, Alexandros Beskos, Dan Crisan +2

We consider the discrete-time filtering problem in scenarios where the observation noise is low or degenerate. We focus on the case where the observation equation is a linear funct…

stat.CO2025

Sequential Markov Chain Monte Carlo for Filtering of State-Space Models with Low or Degenerate Observation Noise

Abylay Zhumekenov, Alexandros Beskos, Dan Crisan +2

We consider the discrete-time filtering problem in scenarios where the observation noise is degenerate or low. More precisely, one is given access to a discrete time observation se…

math.NA2025

Asymptotic Variance in the Central Limit Theorem for Multilevel Markovian Stochastic Approximation

Ajay Jasra, Abylay Zhumekenov

In this note we consider the finite-dimensional parameter estimation problem associated to inverse problems. In such scenarios, one seeks to maximize the marginal likelihood associ…

stat.CO2023

Parallel Selected Inversion for Space-Time Gaussian Markov Random Fields

Abylay Zhumekenov, Elias T. Krainski, Håvard Rue

Performing Bayesian inference on large spatio-temporal models requires extracting inverse elements of large sparse precision matrices for marginal variances, as well as estimating…

cs.NE2019

Fourier Neural Networks: A Comparative Study

Abylay Zhumekenov, Malika Uteuliyeva, Olzhas Kabdolov +3

We review neural network architectures which were motivated by Fourier series and integrals and which are referred to as Fourier neural networks. These networks are empirically eva…