The Experts below are selected from a list of 312 Experts worldwide ranked by ideXlab platform

Manon Costa - One of the best experts on this subject based on the ideXlab platform.

  • A piecewise Deterministic Model for a prey-predator community
    Annals of Applied Probability, 2016
    Co-Authors: Manon Costa
    Abstract:

    We are interested in prey-predator communities where the predator population evolves much faster than the prey's (e.g. insect-tree communities). We introduce a piecewise Deterministic Model for these prey-predator communities that arises as a limit of a microscopic Model when the number of predators goes to infinity. We prove that the process has a unique invariant probability measure and that it is exponentially ergodic. Further on, we rescale the predator dynamics in order to Model predators of smaller size. This slow-fast system converges to a community process in which the prey dynamics is averaged on the predator equilibria. This averaged process admits an invariant probability measure which can be computed explicitly. We use numerical simulations to study the convergence of the invariant probability measures of the rescaled processes.

  • A piecewise Deterministic Model for a prey-predator community
    The Annals of Applied Probability, 2016
    Co-Authors: Manon Costa
    Abstract:

    We are interested in prey-predator communities where the predator population evolves much faster than the prey's (e.g. insect-tree communities). We introduce a piecewise Deterministic Model for these prey-predator communities that arises as a limit of a microscopic Model when the number of predators goes to infinity. We prove that the process has a unique invariant probability measure and that it is exponentially ergodic. Further on, we rescale the predator dynamics in order to Model predators of smaller size. This slow-fast system converges to a community process in which the prey dynamics is averaged on the predator equilibria. This averaged process has an invariant probability measure which can be computed explicitly. We prove that this invariant probability is the weak limit of the invariant probability measures of the rescaled processes.

Christopher J. R. Illingworth - One of the best experts on this subject based on the ideXlab platform.

  • Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.
    Genetics, 2018
    Co-Authors: Nuno R. Nené, Alistair S Dunham, Christopher J. R. Illingworth
    Abstract:

    A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that Deterministic Models of evolution may be effective in capturing the effects of selection even under complex Models of demography, suggesting the more general application of Deterministic approaches to inference. Responding to this literature, we here note a case in which a Deterministic Model of evolution may give highly misleading inferences, resulting from the nonDeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-Deterministic Model. Applying our Model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our Model to sequence data from an evolutionary experiment. We outline scenarios in which our Model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular Deterministic Model.

  • A delay-Deterministic Model for inferring fitness effects from time-resolved genome sequence data
    2017
    Co-Authors: Nuno R. Nené, Alistair S Dunham, Christopher J. R. Illingworth
    Abstract:

    ABSTRACT A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that Deterministic Models of evolution may be effective in capturing the effects of selection even under complex Models of demography, suggesting the more general application of Deterministic approaches to inference. Responding to this literature, we here note a case in which a Deterministic Model of evolution may give highly misleading inferences, resulting from the non-Deterministic properties of mutation in a finite population. We propose an alternative approach which corrects for this error, which we denote the delay-Deterministic Model. Applying our Model to a simple evolutionary system we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our Model to sequence data from an evolutionary experiment. We outline scenarios in which our Model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular Deterministic Model.

Chai Da-pen - One of the best experts on this subject based on the ideXlab platform.

  • Simulation of Deterministic Model and Stochastic Model of Additional Cost for Grid-Integration of Wind Power
    Power system technology, 2014
    Co-Authors: Chai Da-pen
    Abstract:

    Large-scale development of wind power depends on the economy of wind power generation greatly,so it is of important practical significance for orderly development of wind power industry to research the additional cost for grid-connection of wind farm and its impacting factors,and find the way to reduce the cost of wind power generation. From the perspective of power grid operation,taking total cost of power grid as the objective,and the resource reserves,the balance of power and electricity quantity,reserve capacity and hydropower generation scheduling as constraints,a Deterministic Model for the research on additional cost for wind power generation is constructed,and on this basis the scenario tree is led in to describe the particularity of wind power generation and a stochastic Model is built. Utilizing calculation example and CPLEX equation solver the impacts of different energy policies on yearly generated energy of various types of wind power generating units and additional cost for grid-connection of wind farms are simulated. Simulation results show that as for the research on power grid containing wind farms the stochastic Model possesses higher applicability than Deterministic Model.

Nuno R. Nené - One of the best experts on this subject based on the ideXlab platform.

  • Inferring Fitness Effects from Time-Resolved Sequence Data with a Delay-Deterministic Model.
    Genetics, 2018
    Co-Authors: Nuno R. Nené, Alistair S Dunham, Christopher J. R. Illingworth
    Abstract:

    A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that Deterministic Models of evolution may be effective in capturing the effects of selection even under complex Models of demography, suggesting the more general application of Deterministic approaches to inference. Responding to this literature, we here note a case in which a Deterministic Model of evolution may give highly misleading inferences, resulting from the nonDeterministic properties of mutation in a finite population. We propose an alternative approach that acts to correct for this error, and which we denote the delay-Deterministic Model. Applying our Model to a simple evolutionary system, we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our Model to sequence data from an evolutionary experiment. We outline scenarios in which our Model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular Deterministic Model.

  • A delay-Deterministic Model for inferring fitness effects from time-resolved genome sequence data
    2017
    Co-Authors: Nuno R. Nené, Alistair S Dunham, Christopher J. R. Illingworth
    Abstract:

    ABSTRACT A common challenge arising from the observation of an evolutionary system over time is to infer the magnitude of selection acting upon a specific genetic variant, or variants, within the population. The inference of selection may be confounded by the effects of genetic drift in a system, leading to the development of inference procedures to account for these effects. However, recent work has suggested that Deterministic Models of evolution may be effective in capturing the effects of selection even under complex Models of demography, suggesting the more general application of Deterministic approaches to inference. Responding to this literature, we here note a case in which a Deterministic Model of evolution may give highly misleading inferences, resulting from the non-Deterministic properties of mutation in a finite population. We propose an alternative approach which corrects for this error, which we denote the delay-Deterministic Model. Applying our Model to a simple evolutionary system we demonstrate its performance in quantifying the extent of selection acting within that system. We further consider the application of our Model to sequence data from an evolutionary experiment. We outline scenarios in which our Model may produce improved results for the inference of selection, noting that such situations can be easily identified via the use of a regular Deterministic Model.

Sergei S. Pilyugin - One of the best experts on this subject based on the ideXlab platform.