We present an inverse method to construct large classes of chaotic invariant sets together with their exact statistics. The associated dynamical systems are characterized by a probability distribution and a two-form. While our emphasis is on classical systems, we briefly speculate about possible applications to quantum field theory, in the context of generalizations of stochastic quantization.

In this note we consider β-ensembles with real analytic potential and arbitrary inverse temperature β, and review some recent universality results for these measures, obtained in joint works with L. Erdős and H.-T. Yau. In the limit of a large number of particles, the local eigenvalues statistics in the bulk are universal: they coincide with the spacing statistics for the Gaussian β-ensembles. ...

We study the full-counting statistics of current of large open systems through the application of random-matrix theory to transition-rate matrices. We develop a method for calculating the ensemble-averaged current-cumulant generating functions based on an expansion in terms of the inverse system size. We investigate how different symmetry properties and different counting schemes affect the res...

Automatic glottal inverse filtering with the Markov chain Monte Carlo method Harri Auvinen a, Tuomo Raitio b,∗, Manu Airaksinen b, Samuli Siltanen a, Brad H. Story c, Paavo Alku b a Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland b Department of Signal Processing and Acoustics, Aalto University, Espoo, Finland c Department of Speech and Hearing Sciences, Univ...

This paper considers the problem of estimation and inference in semiparametric varying coefficients models when the response variable is subject to random censoring. The paper proposes an estimator based on combining inverse probability censoring weighting and profile least squares estimation. The resulting estimator is shown to be asymptotically normal. The paper proposes three test statistics...

A novel methodology that combines recent advances in computational statistics and reduced-order modeling is presented to explore the application of Bayesian statistical inference to a stochastic inverse problem in radiative heat transfer. The underlying objective of this work is to reveal the potential of using statistical approaches, mainly Bayesian computational statistics and spatial statist...

The generation of meteorological surfaces from point-source data is a difficult but necessary step required for modeling ecological and hydrological processes across landscapes. To date, procedures to acquire, transform, and display meteorological information geographically have been specifically tailored to individual studies. Here we offer a flexible, integrated system that employs a relation...

The dynamical likelihood method for analysis of high energy collider events is reformulated. The method is to reconstruct the elementary parton state from observed quantities. The basic assumption is that each of final state partons occupies a unit phase volume. The parton kinematics is statistically reconstructed using (a) virtual masses of resonant partons and (b) parton kinematic quantities ...

We implement semiparametric random censorship model aided inference for censored median regression models. This is based on the idea that, when the censoring is specified by a common distribution, a semiparametric survival function estimator acts as an improved weight in the so-called inverse censoring weighted estimating function. We show that the proposed method will always produce estimates ...