Zobrazeno 1 - 10
of 268
pro vyhledávání: '"Papageorgiou Ioannis"'
Autor:
Papageorgiou, Ioannis
We study Replica Mean Field limits for a neural system of infinitely many neurons with both inhibitory and excitatory interactions. As a result we obtain an analytical characterisation of the invariant state. In particular we focus on the Galves-L\"o
Externí odkaz:
http://arxiv.org/abs/2401.10955
A hierarchical Bayesian framework is introduced for developing rich mixture models for real-valued time series, partly motivated by important applications in financial time series analysis. At the top level, meaningful discrete states are identified
Externí odkaz:
http://arxiv.org/abs/2308.00913
Estimating the entropy rate of discrete time series is a challenging problem with important applications in numerous areas including neuroscience, genomics, image processing and natural language processing. A number of approaches have been developed
Externí odkaz:
http://arxiv.org/abs/2212.06705
A new Bayesian modelling framework is introduced for piece-wise homogeneous variable-memory Markov chains, along with a collection of effective algorithmic tools for change-point detection and segmentation of discrete time series. Building on the rec
Externí odkaz:
http://arxiv.org/abs/2203.04341
We revisit the Bayesian Context Trees (BCT) modelling framework for discrete time series, which was recently found to be very effective in numerous tasks including model selection, estimation and prediction. A novel representation of the induced post
Externí odkaz:
http://arxiv.org/abs/2202.02239
Real-valued time series are ubiquitous in the sciences and engineering. In this work, a general, hierarchical Bayesian modelling framework is developed for building mixture models for times series. This development is based, in part, on the use of co
Externí odkaz:
http://arxiv.org/abs/2106.03023
Autor:
Kontoyiannis, Ioannis, Mertzanis, Lambros, Panotopoulou, Athina, Papageorgiou, Ioannis, Skoularidou, Maria
We develop a new Bayesian modelling framework for the class of higher-order, variable-memory Markov chains, and introduce an associated collection of methodological tools for exact inference with discrete time series. We show that a version of the co
Externí odkaz:
http://arxiv.org/abs/2007.14900
In this work we study asymptotic properties of a long range memory random walk known as elephant random walk. First we prove recurrence and positive recurrence for the elephant random walk. Then, we establish the transience regime of the model. Final
Externí odkaz:
http://arxiv.org/abs/1910.03142
Autor:
Papageorgiou, Ioannis
Publikováno v:
Markov Processes Relat. Fields, 29, 435-456, 2023
We consider an infinite system of spiking neurons with a drift and both excitatory and inhibitory connections. We study conditions for non-explosiveness and the uniqueness of the invariant measure. In particular, we examine conditions that allow this
Externí odkaz:
http://arxiv.org/abs/1907.09012
We study the infinite-dimensional log-Sobolev inequality for spin systems on $\mathbb{Z}^d$ with interactions of power higher than quadratic. We assume that the one site measure without a boundary $e^{-\phi(x)}dx/Z$ satisfies a log-Sobolev inequality
Externí odkaz:
http://arxiv.org/abs/1906.11980