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pro vyhledávání: '"Whiteley, Nick"'
We present a new representation learning framework, Intensity Profile Projection, for continuous-time dynamic network data. Given triples $(i,j,t)$, each representing a time-stamped ($t$) interaction between two entities ($i,j$), our procedure return
Externí odkaz:
http://arxiv.org/abs/2306.06155
In this paper we offer a new perspective on the well established agglomerative clustering algorithm, focusing on recovery of hierarchical structure. We recommend a simple variant of the standard algorithm, in which clusters are merged by maximum aver
Externí odkaz:
http://arxiv.org/abs/2305.15022
Recent work has shown that sparse graphs containing many triangles cannot be reproduced using a finite-dimensional representation of the nodes, in which link probabilities are inner products. Here, we show that such graphs can be reproduced using an
Externí odkaz:
http://arxiv.org/abs/2210.15277
The Manifold Hypothesis is a widely accepted tenet of Machine Learning which asserts that nominally high-dimensional data are in fact concentrated near a low-dimensional manifold, embedded in high-dimensional space. This phenomenon is observed empiri
Externí odkaz:
http://arxiv.org/abs/2208.11665
Addressing the challenge of scaling-up epidemiological inference to complex and heterogeneous models, we introduce Poisson Approximate Likelihood (PAL) methods. In contrast to the popular ODE approach to compartmental modelling, in which a large popu
Externí odkaz:
http://arxiv.org/abs/2205.13602
Given a graph or similarity matrix, we consider the problem of recovering a notion of true distance between the nodes, and so their true positions. We show that this can be accomplished in two steps: matrix factorisation, followed by nonlinear dimens
Externí odkaz:
http://arxiv.org/abs/2106.01260
Statisticians often use Monte Carlo methods to approximate probability distributions, primarily with Markov chain Monte Carlo and importance sampling. Sequential Monte Carlo samplers are a class of algorithms that combine both techniques to approxima
Externí odkaz:
http://arxiv.org/abs/2007.11936
Autor:
Whiteley, Nick, Rimella, Lorenzo
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractability. The method is applicable to a class of discret
Externí odkaz:
http://arxiv.org/abs/2006.13700
Autor:
Rimella, Lorenzo, Whiteley, Nick
We define an evolving in time Bayesian neural network called a Hidden Markov neural network. The weights of a feed-forward neural network are modelled with the hidden states of a Hidden Markov model, whose observed process is given by the available d
Externí odkaz:
http://arxiv.org/abs/2004.06963
Autor:
Whiteley, Nick
This note outlines a method for clustering time series based on a statistical model in which volatility shifts at unobserved change-points. The model accommodates some classical stylized features of returns and its relation to GARCH is discussed. Clu
Externí odkaz:
http://arxiv.org/abs/1906.10372