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We consider the Markov chain approximations for singular stable-like processes. First we obtain properties of some Markov chains. Then we construct the approximating Markov chains and give a necessary condition for weak convergence of these…

Probability · Mathematics 2012-10-11 Fangjun Xu

We develop a general method for extending Markov processes to a larger state space such that the added points form a polar set. The so obtained extension is an improvement on the standard trivial extension in which case the process is made…

Probability · Mathematics 2019-09-06 Lucian Beznea , Iulian Cîmpean , Michael Röckner

It is possible to construct a double indexed process with sample paths a surface of a family of subordinators obtained by subordination. We study here a branch of this subordination process. This opens martingale methods on symbolic…

Probability · Mathematics 2009-02-13 Nicolas Bouleau

We introduce Markov substitute processes, a new model at the crossroad of statistics and formal grammars, and prove its main property : Markov substitute processes with a given support form an exponential family.

Machine Learning · Statistics 2016-03-28 Olivier Catoni , Thomas Mainguy

Potential theory is a central tool to understand and analyse Markov processes. In this article, we develop its probabilistic counterpart for branching Markov chains. Specifically, we examine versions of quasi-processes or interlacements…

Probability · Mathematics 2023-11-07 Steffen Dereich , Martin Maiwald

We develop a regression based primal-dual martingale approach for solving finite time horizon MDPs with general state and action space. As a result, our method allows for the construction of tight upper and lower biased approximations of…

Numerical Analysis · Mathematics 2022-10-05 Denis Belomestny , John Schoenmakers

In this paper we survey the almost sure central limit theorem and its functional form (quenched) for stationary and ergodic processes. For additive functionals of a stationary and ergodic Markov chain these theorems are known under the…

Probability · Mathematics 2013-04-17 Magda Peligrad

Most successful applications of deep learning involve similar training and test conditions. However, tasks such as biological sequence design involve searching for sequences that improve desirable properties beyond previously known values,…

Machine Learning · Computer Science 2025-05-27 Sophia Hager , Aleem Khan , Andrew Wang , Nicholas Andrews

Semi-Markov models are widely used for survival analysis and reliability analysis. In general, there are two competing parameterizations and each entails its own interpretation and inference properties. On the one hand, a semi-Markov…

Methodology · Statistics 2021-01-01 Azam Asanjarani , Benoit Liquet , Yoni Nazarathy

As an alternative to the well-known methods of "chaining" and "bracketing" that have been developed in the study of random fields, a new method, which is based on a {\em stochastic maximal inequality} derived by using the formula for…

Probability · Mathematics 2017-08-16 Yoichi Nishiyama

In this study, the orthogonalization process for different inner products is applied to pairwise comparisons. Properties of consistent approximations of a given inconsistent pairwise comparisons matrix are examined. A method of a derivation…

Other Computer Science · Computer Science 2020-02-18 W. W. Koczkodaj , R. Smarzewski , J. Szybowski

This simple note lays out a few observations which are well known in many ways but may not have been said in quite this way before. The basic idea is that when comparing two different Markov chains it is useful to couple them is such a way…

Probability · Mathematics 2017-11-16 James E. Johndrow , Jonathan C. Mattingly

Doubly intractable distributions arise in many settings, for example in Markov models for point processes and exponential random graph models for networks. Bayesian inference for these models is challenging because they involve intractable…

Computation · Statistics 2019-04-03 Jaewoo Park , Murali Haran

Motivated by techniques developed in recent progress on lower bounds for sublinear time algorithms (Behnezhad, Roghani and Rubinstein, STOC 2023, FOCS 2023, and STOC 2024) we introduce and study a new class of randomized algorithmic…

Data Structures and Algorithms · Computer Science 2026-03-19 Amir Azarmehr , Soheil Behnezhad , Alma Ghafari , Madhu Sudan

We construct an aggregated version of the value processes associated with stochastic control problems, where the criterion to optimise is given by solutions to semi-martingale backward stochastic differential equations (BSDEs). The results…

Probability · Mathematics 2025-07-03 Dylan Possamaï , Marco Rodrigues , Alexandros Saplaouras

In this work, we present a general method to establish properties of multi-dimensional continuous-time Markov chains representing stochastic reaction networks. This method consists of grouping states together (via a partition of the state…

Probability · Mathematics 2025-05-27 Guillaume Ballif , Laurent Pfeiffer , Jakob Ruess

In order to describe the extremal behaviour of some stochastic process $X$, approaches from univariate extreme value theory are typically generalized to the spatial domain. In particular, generalized peaks-over-threshold approaches allow…

Methodology · Statistics 2026-01-01 Max Thannheimer , Marco Oesting

In this paper, we introduce a class of processes that contains many natural examples. The interesting feature of such type processes lays on its infinite memory that allows it to record a quite ancient history. Then, using the martingale…

Probability · Mathematics 2025-03-04 Paul Doukhan , Xiequan Fan

In this paper we study time-inhomogeneous affine processes beyond the common assumption of stochastic continuity. In this setting times of jumps can be both inaccessible and predictable. To this end we develop a general theory of finite…

Probability · Mathematics 2018-12-21 Martin Keller-Ressel , Thorsten Schmidt , Robert Wardenga

An infinite system of point particles placed in $\mathds{R}^d$ is studied. The particles are of two types; they perform random walks in the course of which those of distinct types repel each other. The interaction of this kind induces an…

Probability · Mathematics 2022-07-18 Yuri Kozitsky , Michael Röckner