Robust Two-Step Wavelet-Based Inference for Time Series Models [PDF]
Journal of the American Statistical Association, 2020 Latent time series models such as (the independent sum of) ARMA(p, q) models with additional stochastic processes are increasingly used for data analysis in biology, ecology, engineering, and economics.
S. Guerrier +3 more
semanticscholar +1 more source
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
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
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
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
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
Posterior inference for sparse hierarchical non-stationary models [PDF]
Computational Statistics & Data Analysis, 2018 Gaussian processes are valuable tools for non-parametric modelling, where typically an assumption of stationarity is employed. While removing this assumption can improve prediction, fitting such models is challenging.
K. Monterrubio-Gómez +4 more
semanticscholar +1 more source
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 +4 more
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
Statistical Inference for Partially Observed Markov Processes via the R Package pomp [PDF]
, 2015 Partially observed Markov process (POMP) models, also known as hidden Markov models or state space models, are ubiquitous tools for time series analysis.
Ionides, Edward L. +2 more
core +4 more sources