[ΒΙΒΛΙΟ][B] Bayesian non-linear statistical inverse problems
R Nickl - 2023 - statslab.cam.ac.uk
Mathematics in Zurich has a long and distinguished tradition, in which the writing of lecture
notes volumes and research monographs plays a prominent part. The Zurich Lectures in …
notes volumes and research monographs plays a prominent part. The Zurich Lectures in …
Variational Bayes for high-dimensional linear regression with sparse priors
We study a mean-field spike and slab variational Bayes (VB) approximation to Bayesian
model selection priors in sparse high-dimensional linear regression. Under compatibility …
model selection priors in sparse high-dimensional linear regression. Under compatibility …
Statistical guarantees for Bayesian uncertainty quantification in nonlinear inverse problems with Gaussian process priors
Bayesian inference and uncertainty quantification in a general class of nonlinear inverse
regression models is considered. Analytic conditions on the regression model {G (θ): θ∈ Θ} …
regression models is considered. Analytic conditions on the regression model {G (θ): θ∈ Θ} …
Learning interaction kernels in heterogeneous systems of agents from multiple trajectories
Systems of interacting particles, or agents, have wide applications in many disciplines,
including Physics, Chemistry, Biology and Economics. These systems are governed by …
including Physics, Chemistry, Biology and Economics. These systems are governed by …
Spike and slab variational Bayes for high dimensional logistic regression
Variational Bayes (VB) is a popular scalable alternative to Markov chain Monte Carlo for
Bayesian inference. We study a mean-field spike and slab VB approximation of widely used …
Bayesian inference. We study a mean-field spike and slab VB approximation of widely used …
Semiparametric inference using fractional posteriors
We establish a general Bernstein-von Mises theorem for approximately linear
semiparametric functionals of fractional posterior distributions based on nonparametric …
semiparametric functionals of fractional posterior distributions based on nonparametric …
Mean-field nonparametric estimation of interacting particle systems
This paper concerns the nonparametric estimation problem of the distribution-state
dependent drift vector field in an interacting $ N $-particle system. Observing single …
dependent drift vector field in an interacting $ N $-particle system. Observing single …
[PDF][PDF] Bayesian nonparametric statistics, St-Flour lecture notes
IÃĢ Castillo - arxiv preprint arxiv:2402.16422, 2024 - arxiv.org
arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 1 Bay!ian nonparamet"c #at$tics St-Flour
lecture notes Ismaël Castillo arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 2 2 Principe. Si …
lecture notes Ismaël Castillo arxiv:2402.16422v1 [math.ST] 26 Feb 2024 Page 2 2 Principe. Si …
Sup-norm adaptive drift estimation for multivariate nonreversible diffusions
C Aeckerle-Willems, C Strauch - The Annals of Statistics, 2022 - projecteuclid.org
The supplementary file contains the proofs of the presented results. With respect to
numbering, notation, and context it is written as a seamless continuation of the main article …
numbering, notation, and context it is written as a seamless continuation of the main article …
Statistical Spatially Inhomogeneous Diffusion Inference
Inferring a diffusion equation from discretely observed measurements is a statistical
challenge of significant importance in a variety of fields, from single-molecule tracking in …
challenge of significant importance in a variety of fields, from single-molecule tracking in …