Results 131 to 140 of about 720,413 (297)
On Epistemics in Expected Free Energy for Linear Gaussian State Space Models. [PDF]
Entropy (Basel), 2021 Koudahl MT, Kouw WM, de Vries B.
europepmc +1 more source
Inference for Adaptive Time Series Models: Stochastic Volatility and Conditionally Gaussian State Space Form [PDF]
In this paper we replace the Gaussian errors in the standard Gaussian, linear state space model with stochastic volatility processes. This is called a GSSF-SV model.
Neil Shephard, Charles S. Bos
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From tumor‐centric to ecosystem‐based hypotheses in brain tumor research and care
Molecular Oncology, EarlyView.Primary brain tumors, whether in adults or children, present a major challenge because of their dramatic prognosis and the ongoing lack of efficient therapeutic approaches. In recent years, a shift has occurred from tumor‐centric concepts to a more holistic view of these tumors as dynamic ecosystems.
Julie Gavard +8 more
wiley +1 more source
Ecology and EvolutionThe recovery of dead marked individuals, either alone or in combination with encounters of these individuals while alive, is an important source of data for estimating survival in birds, mammals, and fish.
Michael Schaub, Jaume A. Badia‐Boher
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EntropyAccurate pest classification is critical for precision agriculture, yet existing deep learning methods face challenges including computational inefficiency from uniform sample processing and inadequate modeling of complex feature relationships.
Jixiang Zou +3 more
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State Space Modeling Using SsfPack in S+FinMetrics 3.0
This paper presents two illustrations of state space modeling in S-PLUS using the SsfPack 3.0 routines implemented in S+FinMetrics 3.0. The state space modeling functions in S+FinMetrics/SsfPack are extremely flexible and powerful and can be used for a ...
Eric W. Zivot
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, 2011 In this work it is constructed a hydro-meteorological factor to improve the adjustment of statistical time series models, such as state space models, of water quality variables by observing hydrological series (recorded in time and space) in a River ...
Gonçalves, A. Manuela, Costa, Marco
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State space modelling of extreme values with particle filters [PDF]
, 2009 State space models are a flexible class of Bayesian model that can be used to smoothly capture non-stationarity. Observations are assumed independent given a latent state process so that their distribution can change gradually over time. Sequential Monte
Wyncoll, David P.
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Molecular Oncology, EarlyView.Detecting circulating tumor cells (CTCs) in blood before surgery may help predict outcomes in patients with head and neck squamous cell carcinoma (HNSCC). Here, we show when combined with tumor size and lymph node involvement from routine imaging, CTC status identifies high‐risk patients with poorer survival—offering a simple, minimally invasive tool ...
Susanne Flach +9 more
wiley +1 more source
State-space analysis of soil data: an approach based on space-varying regression models
Scientia Agricola, 2003 The assessment of the relationship among soil properties (such as total nitrogen and organic carbon) taken along lines called transects is a subject of great interest in agricultural experimentation.
Luís Carlos Timm +4 more
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