Results 11 to 20 of about 65,665 (164)

The value of probabilistic prediction [PDF]

Atmospheric Science Letters, 2008
AbstractProbabilistic forecasts designed to estimate the probability density function of forecast states are potentially more valuable than single forecasts because (1) they can predict not only the most likely outcome but also the probability of occurrence of extreme and rare events and (2) probabilistic forecasts issued on consecutive days are ...
Roberto Buizza
exaly   +3 more sources

Multi-Output Probabilistic Prediction of Drug Side Effects Using Classical Machine Learning Algorithms [PDF]

Pharmaceuticals
Introduction: Drug side effects are a relevant problem for patient safety and public health, and traditional methods have limitations in capturing complex patterns between clinical and pharmacological variables.
Diego Quiguango Farias, Juan Sarasti Espejo, Marlene Arce Salcedo, Byron Velasquez Ron   +3 more
doaj   +2 more sources

Probabilistic prediction by means of the propagation of response variable uncertainty through a Monte Carlo approach in regression random forest: Application to soil moisture regionalization

Frontiers in Environmental Science, 2023
Probabilistic predictions aim to produce a prediction interval with probabilities associated with each possible outcome instead of a single value for each outcome.
Ségolène Dega, Peter Dietrich, Peter Dietrich, Martin Schrön, Hendrik Paasche   +4 more
doaj   +3 more sources

Semantic Probabilistic Inference of Predictions

Известия Иркутского государственного университета: Серия "Математика", 2017
Prediction is one of the most important concepts in science. Predictions obtained from probabilistic knowledge, are described by an inductive-statistical inference (I-S inference).
E. E. Vityaev
doaj   +3 more sources

Optimized decomposition and deep learning with bias correction for reliable runoff point-interval prediction [PDF]

Scientific Reports
Accurate runoff prediction is critical for flood risk management and water resources regulation. This study proposes a probabilistic runoff forecasting framework that integrates optimized signal decomposition, deep learning, bias correction, and ...
Hong Ma, Muhammad Fadhil Marsani, Mohd. Asyraf Mansor, Mohd Shareduwan Mohd Kasihmuddin, Caiyan Long   +4 more
doaj   +2 more sources

Statistical learning and probabilistic prediction in music cognition: mechanisms of stylistic enculturation [PDF]

Annals of the New York Academy of Sciences, 2018
Marcus T Pearce
exaly   +2 more sources

Interpretable wind power probabilistic prediction based on NGBoost

Zhejiang dianli, 2023
To realize the probabilistic prediction of wind power and analyze the influencing factors of the prediction results， this paper proposes a probabilistic prediction method of wind power based on natural gradient boosting （NGBoost） and takes account of ...
LI Bingsheng, PANG Chuanjun, CHENG Dachuang   +2 more
doaj   +1 more source

A probabilistic track model for tropical cyclone risk assessment using multitask learning

Frontiers in Energy Research, 2023
Tropical cyclone (TC) track forecasting is critical for wind risk assessment. This work proposes a novel probabilistic TC track forecasting model based on mixture density network (MDN) and multitask learning (MTL).
Zhou Jian, Xuan Liu, Tianyang Zhao
doaj   +1 more source

A Novel Hybrid Machine Learning Model for Wind Speed Probabilistic Forecasting

Energies, 2022
Accurately capturing wind speed fluctuations and quantifying the uncertainties has important implications for energy planning and management. This paper proposes a novel hybrid machine learning model to solve the problem of probabilistic prediction of ...
Guanjun Liu, Chao Wang, Hui Qin, Jialong Fu, Qin Shen   +4 more
doaj   +1 more source

Probabilistic Forecasting of Offshore Wind Power Based on Dual-stage Attentional LSTM and Joint Quantile Loss Function

Zhongguo dianli, 2023
Probabilistic prediction of offshore wind power is not high in accuracy due to the predetermined threshold limitation of the traditional feature correlation method and the magnitude difference of the quantile loss in each quantile loss.
Xiangjing SU, Haibo YU, Yang FU, Shuxin TIAN, Haiyu LI, Fuhai GENG   +5 more
doaj   +1 more source