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no code implementations • 10 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.

1 code implementation • 14 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.

no code implementations • 24 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].

no code implementations • 24 May 2021 • Marco Ballesio, Ajay Jasra

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

1 code implementation • 24 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

no code implementations • 27 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

no code implementations • 15 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

no code implementations • 26 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)

no code implementations • 17 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

no code implementations • 9 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.

Computation

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