Results 51 to 60 of about 14,044 (266)
Understanding Markov-Switching Rational Expectations Models [PDF]
SSRN Electronic Journal, 2009 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Roger E. A. Farmer +2 more
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Advanced Robotics Research, EarlyView.Robots can learn manipulation tasks from human demonstrations. This work proposes a versatile method to identify the physical interactions that occur in a demonstration, such as sequences of different contacts and interactions with mechanical constraints.
Alex Harm Gert‐Jan Overbeek +3 more
wiley +1 more source
, 1999 Economists continue to debate the importance of nonlinearity to their discipline. When it comes to forecasting levels, unit roots seems to be quite prevalent, and there has been a great deal of skepticism about nonlinear models. See the arguments pro and con in Ramsey (1996).
Mizrach, Bruce, Watkins, James
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The economic performance of cities: A Markov-switching approach [PDF]
Journal of Urban Economics, 2006 Abstract This paper examines the determinants of employment growth in metro areas. To obtain growth rates, we use a Markov-switching model that separates a city's growth path into two distinct phases (high and low), each with its own growth rate. The simple average growth rate over some period is, therefore, the weighted average of the high-phase and
Michael T. Owyang +3 more
openaire +1 more source
Advanced Science, EarlyView.SKALE 2.0 maps disease‐associated protein aggregation as a phase‐resolved structural process, linking mutation‐induced geometric perturbations to nucleation, elongation, and suppressor design. Across neurodegenerative proteins, the framework reveals cryptic aggregation vulnerabilities, separates phase‐concordant and phase‐switching mutations, and ...
Jia Shen Sio +6 more
wiley +1 more source
Stationarity of multivariate Markov–switching ARMA models [PDF]
Journal of Econometrics, 2001 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Christian Francq, Jean-Michel Zakoïan
openaire +3 more sources
Exact Discrete Stochastic Simulation With Deep‐Learning‐Scale Gradient Optimization
Advanced Science, EarlyView.A 203,796‐parameter gene regulatory network classifies handwritten digits with 98.4% accuracy using exact stochastic dynamics. The framework decouples forward simulation from backward differentiation, making continuous‐time Markov chain models compatible with deep‐learning optimization.
Jose M. G. Vilar, Leonor Saiz
wiley +1 more source
ASYMPTOTICS OF CONTROL PROBLEM FOR THE DIFFUSION PROCESS IN MARKOV ENVIRONMENT
Міжнародний науково-технічний журнал "Проблеми керування та інформатики", 2020 A stochastic optimization procedure and a limit generator of the original problem are constructed for a system of stochastic differential equations with Markov switching and diffusion perturbation with control, which is determined by the condition for ...
Я.М. Чабанюк +2 more
doaj +1 more source
Markov-Switching Three-Pass Regression Filter [PDF]
Journal of Business & Economic Statistics, 2017 We introduce a new approach for the estimation of high-dimensional factor models with regime-switching factor loadings by extending the linear three-pass regression filter to settings where parameters can vary according to Markov processes. The new method, denoted as Markov-switching three-pass regression filter (MS-3PRF), is suitable for datasets with
Guérin, Pierre +2 more
openaire +2 more sources
Advanced Science, EarlyView.As a pilot phase of the Central Asian Genomic Diversity Project, whole‐genome sequencing of 166 individuals from 20 Central Asian and Afghan Hazara populations reveals fine‐scale substructure shaped by repeated trans‐Eurasian migration and admixture. Integrated analyses uncover post‐admixture adaptation, archaic introgression, and medically relevant ...
Mengge Wang +11 more
wiley +1 more source