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 +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 +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
Bayesian Learning and Predictability in a Stochastic Nonlinear Dynamical Model [PDF]
, 2012 Bayesian inference methods are applied within a Bayesian hierarchical modelling framework to the problems of joint state and parameter estimation, and of state forecasting.
Campbell, Edward P. +4 more
core +3 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 +1 more
doaj +1 more source
Untenable nonstationarity: An assessment of the fitness for purpose of trend tests in hydrology [PDF]
, 2018 The detection and attribution of long-term patterns in hydrological time series have been important research topics for decades. A significant portion of the literature regards such patterns as ‘deterministic components’ or ‘trends’ even though the ...
Kilsby, Chris G. +2 more
core +1 more source
Goal-Directed Planning for Habituated Agents by Active Inference Using a Variational Recurrent Neural Network [PDF]
, 2020 It is crucial to ask how agents can achieve goals by generating action plans using only partial models of the world acquired through habituated sensory-motor experiences.
Matsumoto, Takazumi, Tani, Jun
core +3 more sources
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
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. +2 more
core +1 more source
A stochastic space-time model for intermittent precipitation occurrences [PDF]
, 2015 Modeling a precipitation field is challenging due to its intermittent and highly scale-dependent nature. Motivated by the features of high-frequency precipitation data from a network of rain gauges, we propose a threshold space-time $t$ random field (tRF)
Stein, Michael L., Sun, Ying
core +2 more sources