Search Results for author: Ajay Jasra

Found 10 papers, 2 papers with code

Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience

no code implementations10 Oct 2023 Mohamed Maama, Ajay Jasra, Kengo Kamatani

The data are assumed to be observed regularly in time and driven by the SDE model with unknown parameters.

Unbiased Estimation using Underdamped Langevin Dynamics

1 code implementation14 Jun 2022 Hamza Ruzayqat, Neil K. Chada, Ajay Jasra

In this work we consider the unbiased estimation of expectations w. r. t.~probability measures that have non-negative Lebesgue density, and which are known point-wise up-to a normalizing constant.

Multilevel Bayesian Deep Neural Networks

no code implementations24 Mar 2022 Neil K. Chada, Ajay Jasra, Kody J. H. Law, Sumeetpal S. Singh

In this article we consider Bayesian inference associated to deep neural networks (DNNs) and in particular, trace-class neural network (TNN) priors which were proposed by Sell et al. [39].

Bayesian Inference Uncertainty Quantification

Unbiased Estimation of the Gradient of the Log-Likelihood for a Class of Continuous-Time State-Space Models

no code implementations24 May 2021 Marco Ballesio, Ajay Jasra

In this paper, we consider static parameter estimation for a class of continuous-time state-space models.

On Unbiased Estimation for Discretized Models

1 code implementation24 Feb 2021 Jeremy Heng, Ajay Jasra, Kody J. H. Law, Alexander Tarakanov

In this article, we consider computing expectations w. r. t.

Bayesian Inference Computation Numerical Analysis Numerical Analysis Methodology

Log-Normalization Constant Estimation using the Ensemble Kalman-Bucy Filter with Application to High-Dimensional Models

no code implementations27 Jan 2021 Dan Crisan, Pierre Del Moral, Ajay Jasra, Hamza Ruzayqat

Based upon new conditional bias results for the mean of the afore-mentioned methods, we analyze the empirical log-scale normalization constants in terms of their $\mathbb{L}_n-$errors and conditional bias.

Computation Statistics Theory Statistics Theory 65C05, 65C20, 62F99, 62M20, 60G35

Multilevel Particle Filters for the Non-Linear Filtering Problem in Continuous Time

no code implementations15 Jul 2019 Ajay Jasra, Fangyuan Yu, Jeremy Heng

Under assumptions, this can achieve a mean square error of $\mathcal{O}(\epsilon^2)$, for $\epsilon>0$ arbitrary, such that the associated cost is $\mathcal{O}(\epsilon^{-4})$.

Numerical Analysis Numerical Analysis Probability

Unbiased inference for discretely observed hidden Markov model diffusions

no code implementations26 Jul 2018 Neil K. Chada, Jordan Franks, Ajay Jasra, Kody J. H. Law, Matti Vihola

The resulting estimator leads to inference without a bias from the time-discretisation as the number of Markov chain iterations increases.

Bayesian Inference Methodology Probability Computation 65C05 (primary), 60H35, 65C35, 65C40 (secondary)

Parameter Estimation in Hidden Markov Models with Intractable Likelihoods Using Sequential Monte Carlo

no code implementations17 Nov 2013 Sinan Yildirim, Sumeetpal Singh, Thomas Dean, Ajay Jasra

We propose sequential Monte Carlo based algorithms for maximum likelihood estimation of the static parameters in hidden Markov models with an intractable likelihood using ideas from approximate Bayesian computation.

Computation Methodology

Variational inference for sparse spectrum Gaussian process regression

no code implementations9 Jun 2013 Linda S. L. Tan, Victor M. H. Ong, David J. Nott, Ajay Jasra

We develop a fast variational approximation scheme for Gaussian process (GP) regression, where the spectrum of the covariance function is subjected to a sparse approximation.


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