Publications associées à Propriété de Markov

On distributional autoregression and iterated transportation

We consider the problem of defining and fitting models of autoregressive time series of probability distributions on a compact interval of Double-struck capital R. An order-1 autoregressive model in this context is to be understood as a Markov chain, where ...

Wiley2024

On a Finite-Size Neuronal Population Equation

Tilo Schwalger, Valentin Marc Schmutz, Eva Löcherbach

Population equations for infinitely large networks of spiking neurons have a long tradition in theoret-ical neuroscience. In this work, we analyze a recent generalization of these equations to populations of finite size, which takes the form of a nonlinear ...

SIAM PUBLICATIONS2023

Routing and charging game in ride-hailing service with electric vehicles

John Lygeros, Kenan Zhang

This paper studies the routing and charging behaviors of electric vehicles in a competitive ride-hailing market. When the vehicles are idle, they can choose whether to continue cruising to search for passengers, or move a charging station to recharge. The ...

IEEE2023

Quantitative Methods for Omnichannel Decision-Making

Andrey Vasilyev

Omnichannel retail has emerged as the new standard in today's commerce landscape, with retailers integrating their physical and online channels to enhance the customer shopping experience. However, such integration presents significant challenges for retai ...

EPFL2023

A note on universal inference

Anthony Christopher Davison, Timmy Rong Tian Tse

Universal inference enables the construction of confidence intervals and tests without regularity conditions by splitting the data into two parts and appealing to Markov's inequality. Previous investigations have shown that the cost of this generality is a ...

WILEY2022

Hidden Markov Modeling Over Graphs

Ali H. Sayed, Virginia Bordignon, Mert Kayaalp, Stefan Vlaski

This work proposes a multi-agent filtering algorithm over graphs for finite-state hidden Markov models (HMMs), which can be used for sequential state estimation or for tracking opinion formation over dynamic social networks. We show that the difference fro ...

IEEE2022

Predictive user-based relocation through incentives in one-way car-sharing systems

Nikolaos Geroliminis, Patrick Stefan Adriaan Stokkink

Car-sharing systems are an attractive alternative to private vehicles due to their benefits in terms of mobility and sustainability. However, the distribution of vehicles throughout the network in one-way systems is disturbed due to asymmetry and stochasti ...

2021

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Giuseppe Carleo, Riccardo Rossi, Dian Wu

Efficient sampling of complex high-dimensional probability distributions is a central task in computational science. Machine learning methods like autoregressive neural networks, used with Markov chain Monte Carlo sampling, provide good approximations to s ...

AMER PHYSICAL SOC2021

Fast Bayesian estimation of spatial count data models

Prateek Bansal, Rico Krüger

Spatial count data models are used to explain and predict the frequency of phenomena such as traffic accidents in geographically distinct entities such as census tracts or road segments. These models are typically estimated using Bayesian Markov chain Mont ...

2021

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

Kshitij Sharma, Jennifer Kaitlyn Olsen

Many factors influence learners' performance on an activity beyond the knowledge required. Learners' on-task effort has been acknowledged for strongly relating to their educational outcomes, reflecting how actively they are engaged in that activity. Howeve ...

ASSOC COMPUTING MACHINERY2020

A Computational Framework For Two-Dimensional Random Walks With Restarts

Stefano Massei

The treatment of two-dimensional random walks in the quarter plane leads to Markov processes which involve semi-infinite matrices having Toeplitz or block Toeplitz structure plus a low-rank correction. We propose an extension of the framework introduced in ...

SIAM PUBLICATIONS2020

Inference methods for the study of interacting biological oscillators in single-cells

Colas Noé Droin

Biological oscillators are pervasive in biology, covering all aspects of life from enzyme kinetics reactions to population dynamics. Although their behaviour has been intensively studied in the last decades, the recent advances of high-throughput experimen ...

EPFL2020

Multilingual Training and Adaptation in Speech Recognition

Sibo Tong

State-of-the-art acoustic models for Automatic Speech Recognition (ASR) are based on Hidden Markov Models (HMM) and Deep Neural Networks (DNN) and often require thousands of hours of transcribed speech data during training. Therefore, building multilingual ...

EPFL2020

The time-dependent expected reward and deviation matrix of a finite QBD process

Sophie Myriam Hautphenne

Deriving the time-dependent expected reward function associated with a continuous-time Markov chain involves the computation of its transient deviation matrix. In this paper we focus on the special case of a finite quasi-birth-and-death (QBD) process, moti ...

ELSEVIER SCIENCE INC2019

Geometric ergodicity for some space-time max-stable Markov chains

Erwan Fabrice Koch

Max-stable processes are central models for spatial extremes. In this paper, we focus on some space-time max-stable models introduced in Embrechts et al. (2016). The processes considered induce discrete-time Markov chains taking values in the space of cont ...

ELSEVIER SCIENCE BV2019

Unsupervised algorithm for disaggregating low-sampling-rate electricity consumption of households

Christophe Ballif, Nicolas Würsch, Lionel Benjamin Bloch, Jordan Holweger, Marina Dorokhova

Non-intrusive load monitoring (NILM) has been extensively researched over the last decade. The objective of NILM is to identify the power consumption of individual appliances and to detect when particular devices are on or off from measuring the power cons ...

2019

Multigrid Methods Combined With Low-Rank Approximation For Tensor-Structured Markov Chains

Daniel Kressner, Francisco Santos Paredes Quartin de Macedo

Markov chains that describe interacting subsystems suffer from state space explosion but lead to highly structured matrices. In this work, we propose a novel tensor-based algorithm to address such tensor-structured Markov chains. Our algorithm combines a t ...

2018

Efficient Learning from Comparisons

Lucas Maystre

Humans are comparison machines: comparing and choosing an item among a set of alternatives (such as objects or concepts) is arguably one of the most natural ways for us to express our preferences and opinions. In many applications, the analysis of data con ...

EPFL2018

From Infinite to Finite Programs: Explicit Error Bounds with Applications to Approximate Dynamic Programming

Daniel Kuhn, Peyman Mohajerin Esfahani, John Lygeros, Tobias Sutter

We consider linear programming (LP) problems in infinite dimensional spaces that are in general computationally intractable. Under suitable assumptions, we develop an approximation bridge from the infinite-dimensional LP to tractable finite convex programs ...

2018

Low-rank tensor methods for large Markov chains and forward feature selection methods

Francisco Santos Paredes Quartin de Macedo

In the first part of this thesis, we present and compare several approaches for the determination of the steady-state of large-scale Markov chains with an underlying low-rank tensor structure. Such structure is, in our context of interest, associated with ...

EPFL2018