Results 21 to 30 of about 6,001 (210)
Comparison Prediction of Transfer Function Models and State Space Models Using Fuzzy Method [PDF]
المجلة العراقية للعلوم الاحصائية, 2021 The research aims to build dynamic models represented by the transfer function and State Space Models of a single input variable and a single output variable, The input and output variables are represented by the temperatures of the water before the ...
Fahad Subhy, Heyam Hayawi
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Learning Generative State Space Models for Active Inference
Frontiers in Computational Neuroscience, 2020 In this paper we investigate the active inference framework as a means to enable autonomous behavior in artificial agents. Active inference is a theoretical framework underpinning the way organisms act and observe in the real world.
Ozan Çatal +4 more
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Comparison of prediction using Matching Pattern and state space models [PDF]
المجلة العراقية للعلوم الاحصائية, 2022 Predicting future behavior is one of the important topics in statistical sciences due to the need for it in different areas of life, and most countries rely on their development programs on advanced scientific foundations and methods in order to reach ...
heyam hayawi, najlaa saad
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Modeling Volatility Using State Space Models [PDF]
International Journal of Neural Systems, 1997 In time series problems, noise can be divided into two categories: dynamic noise which drives the process, and observational noise which is added in the measurement process, but does not influence future values of the system. In this framework, we show that empirical volatilities (the squared relative returns of prices) exhibit a significant amount of
Jens Timmer, Andreas S. Weigend
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State-space models in estimating Lithuanian Business Cycle
Lietuvos Matematikos Rinkinys, 2005 There is not abstract.
Audronė Jakaitienė
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Granger causality for state-space models [PDF]
Physical Review E, 2015 Granger causality, a popular method for determining causal influence between stochastic processes, is most commonly estimated via linear autoregressive modeling. However, this approach has a serious drawback: if the process being modeled has a moving average component, then the autoregressive model order is theoretically infinite, and in finite sample ...
Barnett, Lionel, Seth, Anil K.
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A STATE SPACE MODEL OF THE ECONOMIC FUNDAMENTALS [PDF]
Computers & Mathematics with Applications, 1989 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Craine, Roger, Bowman, David
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Formulating State Space Models in R with Focus on Longitudinal Regression Models
Journal of Statistical Software, 2006 We provide a language for formulating a range of state space models with response densities within the exponential family. The described methodology is implemented in the R-package sspir. A state space model is specified similarly to a generalized linear
Claus Dethlefsen +1 more
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State‐space models for optical imaging [PDF]
Statistics in Medicine, 2007 AbstractMeasurement of stimulus‐induced changes in activity in the brain is critical to the advancement of neuroscience. Scientists use a range of methods, including electrode implantation, surface (scalp) electrode placement, and optical imaging of intrinsic signals, to gather data capturing underlying signals of interest in the brain.
Kary L, Myers +2 more
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Bellman Filtering for State-Space Models [PDF]
SSRN Electronic Journal, 2020 This article presents a filter for state-space models based on Bellman's dynamic programming principle applied to the mode estimator. The proposed Bellman filter generalises the Kalman filter including its extended and iterated versions, while remaining equally inexpensive computationally.
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