The paper proposes Metropolis adjusted Langevin and Hamiltonian Monte Carlo sampling
methods defined on the Riemann manifold to resolve the shortcomings of existing Monte …

Many scientific fields have experienced growth in the use of stochastic differential equations
(SDEs), also known as diffusion processes, to model scientific phenomena over time. SDEs …

The dynamic Schrödinger bridge problem seeks a stochastic process that defines a
transport between two target probability measures, while optimally satisfying the criteria of …

In financial and actuarial modeling and other areas of application, stochastic differential
equations with jumps have been employed to describe the dynamics of various state …

Inference for Dirichlet process hierarchical models is typically performed using Markov chain
Monte Carlo methods, which can be roughly categorized into marginal and conditional …

Recently, there have been conceptually new developments in Monte Carlo methods through
the introduction of new MCMC and sequential Monte Carlo (SMC) algorithms which are …

We consider the problem of regression learning for deterministic design and independent
random errors. We start by proving a sharp PAC-Bayesian type bound for the exponentially …

The chapters of this volume represent the revised versions of the main papers given at the
seventh Séminaire Européen de Statistique on “Statistics for Stochastic Differential …

Despite progress toward gender equality in the labor market over the past few decades,
gender segregation in labor force composition and labor market outcomes persists …

Suppose a new deck of cards is shuffled. Then as the number of shuffles grows, the
distribution of the deck becomes closer and closer to uniform over the set of permutations of …