8 papers
Rapid Mixing of Stereographic MCMC for Heavy-Tailed Sampling
Federica Milinanni, Tyler Farghly, Jun Yang
Sampling from high-dimensional, heavy-tailed distributions is a fundamental challenge in computational statistics, as many standard Markov chain Monte Carlo (MCMC) methods mix poor…
Enforcing Thermodynamic Consistency in Parameter Optimization and Bayesian Inference of Biochemical Reaction Networks
Federica Milinanni, Andrei Kramer-Miehe, Jan Glaubitz +1
Subcellular signaling pathways regulating, e.g., cell division or synaptic plasticity, are often modeled as dynamical networks of reactions satisfying the law of mass action. To re…
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…