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Proceedings of the 23rd international conference on Machine learning - ICML '06, 2006
A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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A family of probabilistic time series models is developed to analyze the time evolution of topics in large document collections. The approach is to use state space models on the natural parameters of the multinomial distributions that represent the topics.
Blei, David M., Lafferty, John D.
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WIREs Systems Biology and Medicine, 2010
AbstractActin monomers assemble into filaments that structurally support cells as well as drive membrane protrusion for cell movement. Within cells, some actin structures are very dynamic and turn over rapidly, while others are very stable. Even purified actin filament dynamics are complex, and researchers have often turned to mathematical models in ...
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AbstractActin monomers assemble into filaments that structurally support cells as well as drive membrane protrusion for cell movement. Within cells, some actin structures are very dynamic and turn over rapidly, while others are very stable. Even purified actin filament dynamics are complex, and researchers have often turned to mathematical models in ...
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A Dynamic Model of Housing Supply
American Economic Journal: Economic Policy, 2017This paper estimates a dynamic microeconometric model of housing supply. The model features forward-looking landowners who optimally choose both the timing and the nature of construction while taking into account expectations about future prices and ...
A. Murphy
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Econometric Reviews, 2001
This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated ...
Christian Gourieroux, Joanna Jasiak
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This paper introduces nonlinear dynamic factor models for various applications related to risk analysis. Traditional factor models represent the dynamics of processes driven by movements of latent variables, called the factors. Our approach extends this setup by introducing factors defined as random dynamic parameters and stochastic autocorrelated ...
Christian Gourieroux, Joanna Jasiak
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2020 42nd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2020
Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies.
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Cancer is a complex disease that continues to pose formidable challenges to therapeutic interventions. An increased understanding of cancer complexity and in particular tumor growth dynamics is critical to the development of more effective therapies.
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Multivariate Behavioral Research, 2017
Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series.
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Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series.
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1995
Good macroeconomic and financial theorists, like all good theorists, want to get the facts straight before theorizing; hence, the explosive growth in the methodology and application of time-series econometrics in the last twenty-five years. Many factors fueled that growth, ranging from important developments in related fields (see Box and Jenkins, 1970)
Francis X. Diebold, Jose A. Lopez
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Good macroeconomic and financial theorists, like all good theorists, want to get the facts straight before theorizing; hence, the explosive growth in the methodology and application of time-series econometrics in the last twenty-five years. Many factors fueled that growth, ranging from important developments in related fields (see Box and Jenkins, 1970)
Francis X. Diebold, Jose A. Lopez
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A Dynamic Model of Stick-Slip Piezoelectric Actuators Considering the Deformation of Overall System
IEEE transactions on industrial electronics (1982. Print), 2021Xiang-bei Wang, Limin Zhu, Hu Huang
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Annual Review of Organizational Psychology and Organizational Behavior, 2016
Recent effort in organizational psychology and organizational behavior (OPOB) research has placed increasing emphasis on understanding dynamic phenomena and processes. This calls for more and better use of dynamic modeling in OPOB research than before. The goals of this review are to provide an overview of the general forms of dynamic modeling in OPOB ...
Mo Wang, Le Zhou, Zhen Zhang
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Recent effort in organizational psychology and organizational behavior (OPOB) research has placed increasing emphasis on understanding dynamic phenomena and processes. This calls for more and better use of dynamic modeling in OPOB research than before. The goals of this review are to provide an overview of the general forms of dynamic modeling in OPOB ...
Mo Wang, Le Zhou, Zhen Zhang
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