Results 21 to 30 of about 144,645 (228)

Bayesian Inference and Prediction of Wave-induced Ship Motion based on Discrete-frequency Model Approximations

, 2018
In this paper, we investigate the use of a discrete-frequency approximation for stochastic processes, modelling wave-induced ship motion and assess its prediction performance.
Justin M. Kennedy, J. Ford, F. Valentinis, T. Pérez   +3 more
semanticscholar   +1 more source

Detectability of Granger causality for subsampled continuous-time neurophysiological processes. [PDF]

Journal of Neuroscience Methods, 2016
BACKGROUND Granger causality is well established within the neurosciences for inference of directed functional connectivity from neurophysiological data. These data usually consist of time series which subsample a continuous-time biophysiological process.
L. Barnett, A. Seth
semanticscholar   +1 more source

Abductive learning of quantized stochastic processes with probabilistic finite automata

Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2013
We present an unsupervised learning algorithm (GenESeSS) to infer the causal structure of quantized stochastic processes, defined as stochastic dynamical systems evolving over discrete time, and producing quantized observations.
I. Chattopadhyay, Hod Lipson
semanticscholar   +1 more source

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

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

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

Understanding and Comparing Scalable Gaussian Process Regression for Big Data

, 2018
As a non-parametric Bayesian model which produces informative predictive distribution, Gaussian process (GP) has been widely used in various fields, like regression, classification and optimization.
Cai, Jianfei, Liu, Haitao, Ong, Yew-Soon, Wang, Yi   +3 more
core   +1 more source

Resolving the structure of interactomes with hierarchical agglomerative clustering

BMC Bioinformatics, 2011
Background Graphs provide a natural framework for visualizing and analyzing networks of many types, including biological networks. Network clustering is a valuable approach for summarizing the structure in large networks, for predicting unobserved ...
Park Yongjin, Bader Joel S
doaj   +1 more source

INLA or MCMC? A Tutorial and Comparative Evaluation for Spatial Prediction in log-Gaussian Cox Processes [PDF]

, 2012
We investigate two options for performing Bayesian inference on spatial log-Gaussian Cox processes assuming a spatially continuous latent field: Markov chain Monte Carlo (MCMC) and the integrated nested Laplace approximation (INLA). We first describe the
Diggle, Peter J., Taylor, Benjamin M.
core   +1 more source

Hybrid machine learning algorithms accurately predict marine ecological communities

Frontiers in Marine Science
Predicting ecological communities is highly challenging but necessary to establish effective conservation and monitoring programs. This study aims to predict the spatial distribution of nematode associations from 25 m to 2500 m water depth over an area ...
Luciana Erika Yaginuma, Luciana Erika Yaginuma, Fabiane Gallucci, Danilo Cândido Vieira, Paula Foltran Gheller, Simone Brito de Jesus, Thais Navajas Corbisier, Gustavo Fonseca   +7 more
doaj   +1 more source