Results 1 to 10 of about 123,697 (193)

Joint inference of exclusivity patterns and recurrent trajectories from tumor mutation trees

Nature Communications, 2023
Cancer progression is an evolutionary process shaped by both deterministic and stochastic forces. Multi-region and single-cell sequencing of tumors enable high-resolution reconstruction of the mutational history of each tumor and highlight the extensive ...
Xiang Ge Luo, Jack Kuipers, Niko Beerenwinkel   +2 more
doaj   +1 more source

Unsupervised relational inference using masked reconstruction

Applied Network Science, 2023
Problem setting Stochastic dynamical systems in which local interactions give rise to complex emerging phenomena are ubiquitous in nature and society. This work explores the problem of inferring the unknown interaction structure (represented as a graph ...
Gerrit Großmann, Julian Zimmerlin, Michael Backenköhler, Verena Wolf   +3 more
doaj   +1 more source

Psychophysical identity and free energy [PDF]

, 2020
An approach to implementing variational Bayesian inference in biological systems is considered, under which the thermodynamic free energy of a system directly encodes its variational free energy.
Kiefer, Alex B.
core   +2 more sources

The Relation between Granger Causality and Directed Information Theory: A Review

Entropy, 2012
This report reviews the conceptual and theoretical links between Granger causality and directed information theory. We begin with a short historical tour of Granger causality, concentrating on its closeness to information theory.
Pierre-Olivier Amblard, Olivier J. J. Michel   +1 more
doaj   +1 more source

Modeling Space-Time Data Using Stochastic Differential Equations [PDF]

, 2009
This paper demonstrates the use and value of stochastic differential equations for modeling space-time data in two common settings. The first consists of point-referenced or geostatistical data where observations are collected at fixed locations and ...
Duan, Jason A., Gelfand, Alan E., Sirmans, C. F.   +2 more
core   +1 more source

A network inference method for large-scale unsupervised identification of novel drug-drug interactions [PDF]

, 2013
Characterizing interactions between drugs is important to avoid potentially harmful combinations, to reduce off-target effects of treatments and to fight antibiotic resistant pathogens, among others.
Guimera, Roger, Sales-Pardo, Marta
core   +4 more sources

Stochastic Block Models with Multiple Continuous Attributes [PDF]

, 2018
The stochastic block model (SBM) is a probabilistic model for community structure in networks. Typically, only the adjacency matrix is used to perform SBM parameter inference.
Bonacci, Thomas, Kwitt, Roland, Mucha, Peter J., Niethammer, Marc, Stanley, Natalie   +4 more
core   +3 more sources

Stochastic partial differential equation based modelling of large space-time data sets

, 2016
Increasingly larger data sets of processes in space and time ask for statistical models and methods that can cope with such data. We show that the solution of a stochastic advection-diffusion partial differential equation provides a flexible model class ...
Abramowitz, Anderson, Antolik, Applequist, Banerjee, Banerjee, Bardossy, Bell, Berrocal, Borgman, Bremnes, Bronson, Brooks, Brown, Cameletti, Carter, Coe, Cooley, Cramér, Cressie, Cressie, Cressie, Duan, Dudgeon, Folland, Friederichs, Frühwirth-Schnatter, Fuentes, Furrer, Gelfand, Gelfand, Gelman, Gilks, Gneiting, Gneiting, Gneiting, Gneiting, Gneiting, Golightly, Gottlieb, Haberman, Hamill, Hamill, Handcock, Hastings, Heine, Huang, Hutchinson, Johannesson, Jones, Kleiber, Künsch, Lindgren, Ma, Malmberg, Matheson, Metropolis, Neal, Nychka, Paciorek, Paciorek, Palmer, Pedlosky, Ramrez, Robert, Roberts, Roberts, Roberts, Royle, Rue, Rue, Sampson, Sansó, Sansó, Shumway, Sigrist, Simpson, Sloughter, Smith, Solna, Stein, Stein, Stein, Stein, Stensrud, Steppeler, Stidd, Storvik, Stroud, Tobin, Vecchia, Vivar, Whittle, Whittle, Whittle, Wikle, Wikle, Wikle, Wikle, Wikle, Wikle, Wikle, Wilks, Wilks, Xu, Xu, Yussouf, Zheng   +107 more
core   +1 more source

Bayesian Nonlinear Support Vector Machines for Big Data

, 2017
We propose a fast inference method for Bayesian nonlinear support vector machines that leverages stochastic variational inference and inducing points. Our experiments show that the proposed method is faster than competing Bayesian approaches and scales ...
Deutsch, Matthaeus, Galy-Fajou, Theo, Kloft, Marius, Wenzel, Florian   +3 more
core   +1 more source

Domain-Driven Identification of Football Probabilities

Mathematics
Obtaining accurate estimates of the true probabilities of sporting events remains a long-standing problem in sports analytics. In this paper we propose a new domain-driven approach that infers true probabilities from betting odds.
Artur Karimov, Aleksandr Koshkin, Dmitrii Kaplun, Denis Butusov   +3 more
doaj   +1 more source