Search Results for author:
Found 10 papers, 2 papers with code
Multilevel Monte Carlo for a class of Partially Observed Processes in Neuroscience
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
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
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].
Unbiased Estimation of the Gradient of the Log-Likelihood for a Class of Continuous-Time State-Space Models
In this paper, we consider static parameter estimation for a class of continuous-time state-space models.
On Unbiased Estimation for Discretized Models
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
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
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
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
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
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
