Suchergebnisse - Inference from stochastic processes and prediction

Causal inference for multivariate stochastic process prediction von Cabuz, Simona, Abreu, Giuseppe

“… Numerous real world systems of major interest are modeled as sets of analog continuous stochastic processes with delayed and varying causal relationships …”
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Causal inference for multivariate stochastic process prediction von Simona Maria Cabuz, Giuseppe Abreu

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Multivariate Stochastic Rayleigh Process: Computational Aspects, Statistical Inference, Estimation and Prediction Analysis von Chakroune, Yassine, El Azri, Abdenbi, Nafidi, Ahmed, Makroz, Ilyasse

“… The main aim of this paper is to introduce a new multivariate stochastic Rayleigh diffusion process as an extension of the univariate stochastic Rayleigh model, which has been the subject of much …”
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Time series : theory and methods von Brockwell, P. J. (Peter J.), Davis, R. A. (Richard A.)

“… This paperback edition is a reprint of the 1991 edition. Time Series: Theory and Methods is a systematic account of linear time series models and their application to the modeling and prediction of data collected sequentially in time …”
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Bayesian analysis of stochastic process models von Ríos Insua, David, Ruggeri, Fabrizio, Wiper, Michael P.

“… * Looks at inference, prediction and decision making. Researchers, graduate and advanced undergraduate students interested in stochastic processes in fields such as statistics, operations research …”
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Robust Two-Step Wavelet-Based Inference for Time Series Models von Guerrier, Stéphane, Molinari, Roberto, Victoria-Feser, Maria-Pia, Xu, Haotian

“… 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 …”
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An artificial neural network supported stochastic process for degradation modeling and prediction von Liu, Di, Wang, Shaoping

“… •The process parameters are updated by Bayesian inference for online prediction.•Without path information the ANN supported stochastic process is still practical …”
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Multifidelity multilevel Monte Carlo to accelerate approximate Bayesian parameter inference for partially observed stochastic processes von Warne, David J., Prescott, Thomas P., Baker, Ruth E., Simpson, Matthew J.

“… Models of stochastic processes are widely used in almost all fields of science. Theory validation, parameter estimation, and prediction all require model calibration and statistical inference using data …”
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Bayesian LSTM With Stochastic Variational Inference for Estimating Model Uncertainty in Process‐Based Hydrological Models von Li, Dayang, Marshall, Lucy, Liang, Zhongmin, Sharma, Ashish, Zhou, Yan

“… Here, we introduce an alternative to Bayesian MCMC sampling called stochastic variational inference (SVI …”
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Leapfrog Diffusion Model for Stochastic Trajectory Prediction von Mao, Weibo, Xu, Chenxin, Zhu, Qi, Chen, Siheng, Wang, Yanfeng

“… To model the indeterminacy of human behaviors, stochastic trajectory prediction requires a sophisticated multi-modal distribution of future trajectories …”
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Inference and prediction for stochastic models of biological populations undergoing migration and proliferation von Simpson, Matthew J, Plank, Michael J

“… Parameter inference is a critical step in the process of interpreting biological data using mathematical models …”
Weitere Angaben

Effluent trading planning and its application in water quality management: A factor-interaction perspective von Zhang, J.L., Li, Y.P., Zeng, X.T., Huang, G.H., Li, Y., Zhu, Y., Kong, F.L., Xi, M., Liu, J.

“… Bayesian inference is employed for uncertainty analysis of SWAT model parameters and uncertain prediction of nutrient loadings …”
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Data-driven method for real-time prediction and uncertainty quantification of fatigue failure under stochastic loading using artificial neural networks and Gaussian process regression von Farid, Maor

“… •The current manuscript focuses on a data-driven method for real-time fatigue failure prediction under stochastic loadings …”
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Corrections to “On the Nonparametric Prediction of Conditionally Stationary Sequences”, by S. Caires and J.A. Ferreira in Statistical Inference for Stochastic Processes 8 (2005), 151–184

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Collaborative Online RUL Prediction of Multiple Assets With Analytically Recursive Bayesian Inference von Peng, Weiwen, Chen, Yuan, Xu, Ancha, Ye, Zhi-Sheng

“… ) prediction adopt a stochastic process-based degradation model and a computation-intensive parameter estimation method for RUL prediction of a single operating asset …”
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Deep Bayesian stochastic process model for remaining useful life prediction of rolling bearings von Deng, Minqiang, Xu, Meng, Bian, Wenbin, Liu, Dongying, Deng, Aidong

“… The stochastic process model (SPM) offers a promising approach for remaining useful life (RUL …”
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Heterogeneous Hypergraph Variational Autoencoder for Link Prediction von Fan, Haoyi, Zhang, Fengbin, Wei, Yuxuan, Li, Zuoyong, Zou, Changqing, Gao, Yue, Dai, Qionghai

“… Link prediction aims at inferring missing links or predicting future ones based on the currently observed network …”
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High‐Frequency Instruments and Identification‐Robust Inference for Stochastic Volatility Models von Ahsan, Md. Nazmul, Dufour, Jean‐Marie

“… We study parameter inference problems in the proposed framework with nonstationary stochastic volatility and exogenous predictors in the latent volatility process. Identification …”
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Scalable Semisupervised GMM for Big Data Quality Prediction in Multimode Processes von Yao, Le, Ge, Zhiqiang

“… In this paper, a novel variational inference semisupervised Gaussian mixture model (VI-S 2 GMM …”
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Amortized Bayesian inference on generative dynamical network models of epilepsy using deep neural density estimators von Hashemi, Meysam, Vattikonda, Anirudh N., Jha, Jayant, Sip, Viktor, Woodman, Marmaduke M., Bartolomei, Fabrice, Jirsa, Viktor K.

“… Such a parametric simulator is equipped with a stochastic generative process, which itself provides the basis for inference and prediction of the local and global brain dynamics affected by disorders …”
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