The Experts below are selected from a list of 294 Experts worldwide ranked by ideXlab platform

Marcel Van Oijen - One of the best experts on this subject based on the ideXlab platform.

  • bayesian calibration comparison and averaging of six forest models using data from scots pine stands across europe
    Forest Ecology and Management, 2013
    Co-Authors: Marcel Van Oijen, Christopher P O Reyer, Friedrich Bohn, D R Cameron, Gaby Deckmyn, Michael Flechsig, Sanna Harkonen, Florian Hartig, Andreas Huth, Andres Kiviste
    Abstract:

    Abstract Forest management requires prediction of forest growth, but there is no general agreement about which models best predict growth, how to quantify model parameters, and how to assess the uncertainty of model predictions. In this paper, we show how Bayesian calibration (BC), Bayesian model comparison (BMC) and Bayesian model averaging (BMA) can help address these issues. We used six models, ranging from simple parameter-sparse models to complex process-based models: 3PG, 4C, ANAFORE, BASFOR, BRIDGING and FORMIND. For each model, the initial degree of uncertainty about parameter values was expressed in a Prior Probability Distribution. Inventory data for Scots pine on tree height and diameter, with estimates of measurement uncertainty, were assembled for twelve sites, from four countries: Austria, Belgium, Estonia and Finland. From each country, we used data from two sites of the National Forest Inventories (NFIs), and one Permanent Sample Plot (PSP). The models were calibrated using the NFI-data and tested against the PSP-data. Calibration was done both per country and for all countries simultaneously, thus yielding country-specific and generic parameter Distributions. We assessed model performance by sampling from Prior and posterior Distributions and comparing the growth predictions of these samples to the observations at the PSPs. We found that BC reduced uncertainties strongly in all but the most complex model. Surprisingly, country-specific BC did not lead to clearly better within-country predictions than generic BC. BMC identified the BRIDGING model, which is of intermediate complexity, as the most plausible model before calibration, with 4C taking its place after calibration. In this BMC, model plausibility was quantified as the relative Probability of a model being correct given the information in the PSP-data. We discuss how the method of model initialisation affects model performance. Finally, we show how BMA affords a robust way of predicting forest growth that accounts for both parametric and model structural uncertainty.

  • bayesian calibration of process based forest models bridging the gap between models and data
    Tree Physiology, 2005
    Co-Authors: Marcel Van Oijen, Jonathan Rougier, R F Smith
    Abstract:

    Process-based forest models generally have many parameters, multiple outputs of interest and a small underlying empirical database. These characteristics hamper parameterization. Bayesian calibration offers a solution to the calibration problem because it applies to models of any type or size. It provides parameter estimates, with measures of uncertainty and correlation among the parameters. The procedure begins by quantifying the uncertainty about parameter values in the form of a Prior Probability Distribution. Then data on the output variables are used to update the parameter Distribution by means of Bayes' Theorem. This yields a posterior calibrated Distribution for the parameters, which can be summarized in the form of a mean vector and variance matrix. The predictive uncertainty of the model can be quantified by running it with different parameter settings, sampled from the posterior Distribution. In a further step, one may evaluate the posterior Probability of the model itself (rather than that of the parameters) and compare that against the Probability of other models, to aid in model selection or improvement. Bayesian calibration of process-based models cannot be performed analytically, so the posterior parameter Distribution must be approximated in the form of a representative sample of parameter values. This can be achieved by means of Markov Chain Monte Carlo simulation, which is suitable for process-based models because of its simplicity and because it does not require advance knowledge of the shape of the posterior Distribution. Despite the suitability of Bayesian calibration, the technique has rarely been used in forestry research. We introduce the method, using the example of a typical forest model. Further, we show that reductions in parameter uncertainty, and thus in output uncertainty, can be effected by increasing the variety of data, increasing the accuracy of measurements and increasing the length of time series.

Ramandeep S Johal - One of the best experts on this subject based on the ideXlab platform.

  • Expected behavior of quantum thermodynamic machines with Prior information.
    Physical review. E Statistical nonlinear and soft matter physics, 2012
    Co-Authors: George Thomas, Ramandeep S Johal
    Abstract:

    We estimate the expected behavior of the quantum model of a heat engine when we have incomplete information about external macroscopic parameters such as the magnetic field controlling the intrinsic energy scales of the working medium. We explicitly derive the Prior Probability Distribution for these unknown parameters ai (i=1,2). Based on a few simple assumptions, the Prior Probability Distribution is found to be of the form Π(ai)∝1/ai. By calculating the expected values of various physical quantities related to this engine, we find that the expected behavior of the quantum model exhibits thermodynamiclike features. This leads us to a surprising proposal that incomplete information quantified as an appropriate Prior Distribution can lead us to expect classical thermodynamic behavior in quantum models.

  • expected behavior of quantum thermodynamic machines with Prior information
    Physical Review E, 2012
    Co-Authors: George Thomas, Ramandeep S Johal
    Abstract:

    We estimate the expected behavior of the quantum model of a heat engine when we have incomplete information about external macroscopic parameters such as the magnetic field controlling the intrinsic energy scales of the working medium. We explicitly derive the Prior Probability Distribution for these unknown parameters ${a}_{i}\phantom{\rule{0.28em}{0ex}}\phantom{\rule{4pt}{0ex}}(i=1,2)$. Based on a few simple assumptions, the Prior Probability Distribution is found to be of the form $\ensuremath{\Pi}({a}_{i})\ensuremath{\propto}1/{a}_{i}$. By calculating the expected values of various physical quantities related to this engine, we find that the expected behavior of the quantum model exhibits thermodynamiclike features. This leads us to a surprising proposal that incomplete information quantified as an appropriate Prior Distribution can lead us to expect classical thermodynamic behavior in quantum models.

R F Smith - One of the best experts on this subject based on the ideXlab platform.

  • bayesian calibration of process based forest models bridging the gap between models and data
    Tree Physiology, 2005
    Co-Authors: Marcel Van Oijen, Jonathan Rougier, R F Smith
    Abstract:

    Process-based forest models generally have many parameters, multiple outputs of interest and a small underlying empirical database. These characteristics hamper parameterization. Bayesian calibration offers a solution to the calibration problem because it applies to models of any type or size. It provides parameter estimates, with measures of uncertainty and correlation among the parameters. The procedure begins by quantifying the uncertainty about parameter values in the form of a Prior Probability Distribution. Then data on the output variables are used to update the parameter Distribution by means of Bayes' Theorem. This yields a posterior calibrated Distribution for the parameters, which can be summarized in the form of a mean vector and variance matrix. The predictive uncertainty of the model can be quantified by running it with different parameter settings, sampled from the posterior Distribution. In a further step, one may evaluate the posterior Probability of the model itself (rather than that of the parameters) and compare that against the Probability of other models, to aid in model selection or improvement. Bayesian calibration of process-based models cannot be performed analytically, so the posterior parameter Distribution must be approximated in the form of a representative sample of parameter values. This can be achieved by means of Markov Chain Monte Carlo simulation, which is suitable for process-based models because of its simplicity and because it does not require advance knowledge of the shape of the posterior Distribution. Despite the suitability of Bayesian calibration, the technique has rarely been used in forestry research. We introduce the method, using the example of a typical forest model. Further, we show that reductions in parameter uncertainty, and thus in output uncertainty, can be effected by increasing the variety of data, increasing the accuracy of measurements and increasing the length of time series.

George Thomas - One of the best experts on this subject based on the ideXlab platform.

  • Expected behavior of quantum thermodynamic machines with Prior information.
    Physical review. E Statistical nonlinear and soft matter physics, 2012
    Co-Authors: George Thomas, Ramandeep S Johal
    Abstract:

    We estimate the expected behavior of the quantum model of a heat engine when we have incomplete information about external macroscopic parameters such as the magnetic field controlling the intrinsic energy scales of the working medium. We explicitly derive the Prior Probability Distribution for these unknown parameters ai (i=1,2). Based on a few simple assumptions, the Prior Probability Distribution is found to be of the form Π(ai)∝1/ai. By calculating the expected values of various physical quantities related to this engine, we find that the expected behavior of the quantum model exhibits thermodynamiclike features. This leads us to a surprising proposal that incomplete information quantified as an appropriate Prior Distribution can lead us to expect classical thermodynamic behavior in quantum models.

  • expected behavior of quantum thermodynamic machines with Prior information
    Physical Review E, 2012
    Co-Authors: George Thomas, Ramandeep S Johal
    Abstract:

    We estimate the expected behavior of the quantum model of a heat engine when we have incomplete information about external macroscopic parameters such as the magnetic field controlling the intrinsic energy scales of the working medium. We explicitly derive the Prior Probability Distribution for these unknown parameters ${a}_{i}\phantom{\rule{0.28em}{0ex}}\phantom{\rule{4pt}{0ex}}(i=1,2)$. Based on a few simple assumptions, the Prior Probability Distribution is found to be of the form $\ensuremath{\Pi}({a}_{i})\ensuremath{\propto}1/{a}_{i}$. By calculating the expected values of various physical quantities related to this engine, we find that the expected behavior of the quantum model exhibits thermodynamiclike features. This leads us to a surprising proposal that incomplete information quantified as an appropriate Prior Distribution can lead us to expect classical thermodynamic behavior in quantum models.

Navid Mostoufi - One of the best experts on this subject based on the ideXlab platform.

  • Fusion of micro-macro data for fault diagnosis of a sweetening unit using Bayesian network
    Chemical Engineering Research and Design, 2016
    Co-Authors: Mahdieh Askarian, Reza Zarghami, Farhang Jalali Farahani, Navid Mostoufi
    Abstract:

    Present study addresses a fault diagnosis system based on micro-macro data for monitoring chemical plants. Macro data are encapsulation of process history in terms of Prior Probability Distribution of faults which is achieved using the fault tree analysis. Then, the fault diagnosis system is developed based on an imbalanced dataset, established by frequent and rare faults. In addition, micro data, records of sensors at each time step, are used to predict faults. The Bayesian network is proposed to integrate micro-macro data for diagnostic purposes. Efficiency of the proposed framework was evaluated for an industrial gas sweetening unit. It was shown that the diagnostic performance of the proposed approach is remarkable. Thus, it was concluded that fusion of micro-macro data enhances the performance of the fault diagnosis system. Furthermore, extraction of significant features using the principal components analysis promotes the diagnosis performance. The proposed framework, compared to conventional ones, shows 21% improvement in terms of accuracy. In addition, error bands of fault prediction decreased through implementing a hierarchical strategy.