6 papers
Exploiting Exact Conditionals Improves Conditioning: Provably Fast Mixing Time Bounds By Sampling from the Marginal
Abhijit Chowdhary, Federica Milinanni, Julianne Chung +1
The problem of sampling from a probability distribution arises in many applications such as posterior sampling in hierarchical Bayesian inverse problems and Gaussian processes for…
Large deviation-based tuning schemes for Metropolis-Hastings algorithms
Federica Milinanni
Markov chain Monte Carlo (MCMC) methods are one of the most popular classes of algorithms for sampling from a target probability distribution. A rising trend in recent years consis…
Large deviations for Independent Metropolis Hastings and Metropolis-adjusted Langevin algorithm
Federica Milinanni, Pierre Nyquist
In this paper, we prove large deviation principles for the empirical measures associated with the Independent Metropolis Hastings (IMH) sampler and the Metropolis-adjusted Langevin…
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…