Results 11 to 20 of about 26,939 (224)
Deterministic, stochastic, and mean-field PDE models in neuroscience [PDF]
Frontiers in Computational NeuroscienceLarge neuronal networks demonstrate complex dynamics across multiple scales, ranging from single-neuron excitability and spike-train variability to mesoscopic rhythms and whole-brain activity.
Coşkun Çetin +5 more
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Bayesian inference of mixed Gaussian phylogenetic models [PDF]
BMC BioinformaticsBackground Continuous traits evolution of a group of taxa that are correlated through a phylogenetic tree is commonly modelled using parametric stochastic differential equations to represent deterministic change of traits through time, while ...
Bayu Brahmantio +2 more
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Quantifying Parameter Interdependence in Stochastic Discrete Models of Biochemical Systems
Entropy, 2023 Stochastic modeling of biochemical processes at the cellular level has been the subject of intense research in recent years. The Chemical Master Equation is a broadly utilized stochastic discrete model of such processes.
Samaneh Gholami, Silvana Ilie
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“Exact” and Approximate Methods for Bayesian Inference: Stochastic Volatility Case Study
Entropy, 2021 We conduct a case study in which we empirically illustrate the performance of different classes of Bayesian inference methods to estimate stochastic volatility models.
Yuliya Shapovalova
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PLoS Computational Biology, 2022 Quantifying biochemical reaction rates within complex cellular processes remains a key challenge of systems biology even as high-throughput single-cell data have become available to characterize snapshots of population variability.
Timon Wittenstein +2 more
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Scalable and flexible inference framework for stochastic dynamic single-cell models.
PLoS Computational Biology, 2022 Understanding the inherited nature of how biological processes dynamically change over time and exhibit intra- and inter-individual variability, due to the different responses to environmental stimuli and when interacting with other processes, has been a
Sebastian Persson +7 more
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Retrieval of process rate parameters in the general dynamic equation for aerosols using Bayesian state estimation: BAYROSOL1.0 [PDF]
Geoscientific Model Development, 2021 The uncertainty in the radiative forcing caused by aerosols and its effect on climate change calls for research to improve knowledge of the aerosol particle formation and growth processes.
M. Ozon +5 more
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MethodsX, 2020 The development of an adaptive trait simulator package for inferring trait evolution along a phylogenetic tree is shown. Stochastic processes of the continuous type are broadly applied to modeling trait evolution when the evolutionary relationship among ...
Dwueng-Chwuan Jhwueng
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PLoS Computational Biology, 2022 Quantitative stochastic models of gene regulatory networks are important tools for studying cellular regulation. Such models can be formulated at many different levels of fidelity. A practical challenge is to determine what model fidelity to use in order
Adrien Coulier +3 more
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Inference for nonlinear epidemiological models using genealogies and time series. [PDF]
PLoS Computational Biology, 2011 Phylodynamics - the field aiming to quantitatively integrate the ecological and evolutionary dynamics of rapidly evolving populations like those of RNA viruses - increasingly relies upon coalescent approaches to infer past population dynamics from ...
David A Rasmussen +2 more
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