8 citations · 23 across the 13 of their papers we have counts for
13 papers
A weak convergence approach to large deviations for stochastic approximations
Henrik Hult, Adam Lindhe, Pierre Nyquist +1
The theory of stochastic approximations form the theoretical foundation for studying convergence properties of many popular recursive learning algorithms in statistics, machine lea…
UQSA -- An R-Package for Uncertainty Quantification and Sensitivity Analysis for Biochemical Reaction Network Models
Andrei Kramer, Federica Milinanni, Jeanette Hellgren Kotaleski +3
Biochemical reaction models describing subcellular processes generally come with a large uncertainty. To be able to account for this during the modeling process, we have developed…
A large deviation principle for the empirical measures of Metropolis-Hastings chains
Federica Milinanni, Pierre Nyquist
To sample from a given target distribution, Markov chain Monte Carlo (MCMC) sampling relies on constructing an ergodic Markov chain with the target distribution as its invariant me…
Sensitivity Approximation by the Peano-Baker Series
Olivia Eriksson, Andrei Kramer, Federica Milinanni +1
In this paper we develop a new method for numerically approximating sensitivities in parameter-dependent ordinary differential equations (ODEs). Our approach, intended for situatio…
Quasistationary Distributions and Ergodic Control Problems
Amarjit Budhiraja, Paul Dupuis, Pierre Nyquist +1
We introduce and study the basic properties of two ergodic stochastic control problems associated with the quasistationary distribution (QSD) of a diffusion process relative to…
Large deviations for the empirical measure of the zig-zag process
Joris Bierkens, Pierre Nyquist, Mikola C. Schlottke
The zig-zag process is a piecewise deterministic Markov process in position and velocity space. The process can be designed to have an arbitrary Gibbs type marginal probability den…