Publications related to Markov property

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 ...

Hoboken2024

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

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, Mert Kayaalp, Stefan Vlaski, Virginia Bordignon

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

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

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

Fabio Nobile, Juan Pablo Madrigal Cianci

In the current work we present two generalizations of the Parallel Tempering algorithm, inspired by the so-called continuous-time Infinite Swapping algorithm. Such a method, found its origins in the molecular dynamics community, and can be understood as th ...

MATHICSE2020

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Markov property

On distributional autoregression and iterated transportation

On a Finite-Size Neuronal Population Equation

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

Quantitative Methods for Omnichannel Decision-Making

A note on universal inference

Hidden Markov Modeling Over Graphs

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

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Predicting learners' effortful behaviour in adaptive assessment using multimodal data

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

Graph Chatbot

Chat with Graph Search

A note on universal inference

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

MATHICSE Technical Report : Generalized Parallel Tempering on Bayesian Inverse Problems

On a Finite-Size Neuronal Population Equation

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

Quantitative Methods for Omnichannel Decision-Making

Hidden Markov Modeling Over Graphs

Unbiased Monte Carlo cluster updates with autoregressive neural networks

Predicting learners' effortful behaviour in adaptive assessment using multimodal data