Results 51 to 60 of about 45,001 (165)

Global Sampling for Sequential Filtering over Discrete State Space

EURASIP Journal on Advances in Signal Processing, 2004
In many situations, there is a need to approximate a sequence of probability measures over a growing product of finite spaces. Whereas it is in general possible to determine analytic expressions for these probability measures, the number of computations
Cheung-Mon-Chan Pascal, Moulines Eric
doaj   +1 more source

Multivariable modelling based on statistical and machine learning techniques for monthly precipitation forecasting in the eastern Amazon

Frontiers in Earth Science
BackgroundAccurate precipitation forecasting is crucial for various sectors, such as agriculture, hydrology, and disaster management. In recent years, machine learning (ML) techniques have proven invaluable in improving the accuracy of rainfall ...
Renata Gonçalves Tedeschi, Eduardo Costa de Carvalho, Antonio Vasconcelos Nogueira Neto, Claudia Priscila Wanzeler da Costa, Julio Cezar Goncalves de Freitas, Julio Cezar Goncalves de Freitas, Rafael de Lima Rocha, Rafael de Lima Rocha, Ronnie Cley de Oliveira Alves, Ronnie Cley de Oliveira Alves, Ewerton Cristhian Lima de Oliveira   +10 more
doaj   +1 more source

Autoregressive conditional root model. [PDF]

, 2001
In this paper we develop a time series model which allows long-term disequilibriums to have epochs of non-stationarity, giving the impression that long term relationships between economic variables have temporarily broken down, before they endogenously collapse back towards their long term relationship.
Anders Rahbek, Neil Shephard
openaire   +1 more source

Enhancing Forecasting Accuracy of Financial Time Series by Hybrid VAR and Transfer Function Models [PDF]

The Egyptian Statistical Journal
Our study suggests an approach that integrates vector autoregressive and transfer function models to enhance the modeling and forecasting of financial time series.
Mona Abdel Bary
doaj   +1 more source

Scaling Autoregressive Video Models

CoRR, 2019
International Conference on Learning Representations (ICLR ...
Dirk Weissenborn, Oscar Täckström, Jakob Uszkoreit   +2 more
openaire   +3 more sources

Subsampling Algorithms for Irregularly Spaced Autoregressive Models

Algorithms
With the exponential growth of data across diverse fields, applying conventional statistical methods directly to large-scale datasets has become computationally infeasible.
Jiaqi Liu, Ziyang Wang, HaiYing Wang, Nalini Ravishanker   +3 more
doaj   +1 more source

Mutual Information, the Linear Prediction Model, and CELP Voice Codecs

Information, 2019
We write the mutual information between an input speech utterance and its reconstruction by a code-excited linear prediction (CELP) codec in terms of the mutual information between the input speech and the contributions due to the short-term predictor ...
Jerry Gibson
doaj   +1 more source

Lattice protein folding with variational annealing

Machine Learning: Science and Technology
Understanding the principles of protein folding is a cornerstone of computational biology, with implications for drug design, bioengineering, and the understanding of fundamental biological processes. Lattice protein folding models offer a simplified yet
Shoummo A Khandoker, Estelle M Inack, Mohamed Hibat-Allah   +2 more
doaj   +1 more source

Inspiratory and expiratory elastance in a non-linear autoregressive model of pulmonary mechanics

Current Directions in Biomedical Engineering, 2016
For patients with acute respiratory distress syndrome (ARDS), the use of mathematical models to determine patient-specific ventilator settings can reduce ventilator induced lung injury and improve patient outcomes.
Langdon Ruby, Docherty Paul D., Möller Knut   +2 more
doaj   +1 more source

Diagnosing Errors in Climate Forecast Models Using Forced Autoregressive Models

Journal of Advances in Modeling Earth Systems
Climate models initialized near the observed state typically drift toward their own climatology as the forecast evolves. This drift is commonly corrected through a lead‐time and start‐month dependent bias adjustment, derived from a hindcast data set ...
Timothy DelSole, Michael K. Tippett, Nathaniel C. Johnson   +2 more
doaj   +1 more source