The Experts below are selected from a list of 95718 Experts worldwide ranked by ideXlab platform
Patricia Burkhardt-holm - One of the best experts on this subject based on the ideXlab platform.
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2006Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures. (c) 2005 Elsevier B.V. All rights reserved
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2005Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures.
V Dose - One of the best experts on this subject based on the ideXlab platform.
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Bayesian Probability theory applications in the physical sciences
2014Co-Authors: Wolfgang Von Der Linden, V Dose, Udo Von ToussaintAbstract:From the basics to the forefront of modern research, this book presents all aspects of Probability theory, statistics and data analysis from a Bayesian perspective for physicists and engineers. The book presents the roots, applications and numerical implementation of Probability theory, and covers advanced topics such as maximum entropy distributions, stochastic processes, parameter estimation, model selection, hypothesis testing and experimental design. In addition, it explores state-of-the art numerical techniques required to solve demanding real-world problems. The book is ideal for students and researchers in physical sciences and engineering.
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Background–source separation in astronomical images with Bayesian Probability theory – I. The method
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: F Guglielmetti, Rainer Fischer, V DoseAbstract:A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian Probability theory is applied to gain insight into the co-existence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multiresolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parametrized automatically providing source position, net counts, morphological parameters and their errors. We demonstrate the capability of our method by applying it to three simulated data sets characterized by different background and source intensities. The results of employing two different prior knowledge on the source signal distribution are shown. The probabilistic method allows for the detection of bright and faint sources independently of their morphology and the kind of background. The results from our analysis of the three simulated data sets are compared with other source detection methods. Additionally, the technique is applied to ROSAT All-Sky Survey data.
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background source separation in astronomical images with Bayesian Probability theory i the method
Monthly Notices of the Royal Astronomical Society, 2009Co-Authors: F Guglielmetti, R. Fischer, V DoseAbstract:A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian Probability theory is applied to gain insight into the co-existence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multiresolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parametrized automatically providing source position, net counts, morphological parameters and their errors. We demonstrate the capability of our method by applying it to three simulated data sets characterized by different background and source intensities. The results of employing two different prior knowledge on the source signal distribution are shown. The probabilistic method allows for the detection of bright and faint sources independently of their morphology and the kind of background. The results from our analysis of the three simulated data sets are compared with other source detection methods. Additionally, the technique is applied to ROSAT All-Sky Survey data.
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background source separation in astronomical images with Bayesian Probability theory i the method
arXiv: Instrumentation and Methods for Astrophysics, 2009Co-Authors: F Guglielmetti, R. Fischer, V DoseAbstract:A probabilistic technique for the joint estimation of background and sources with the aim of detecting faint and extended celestial objects is described. Bayesian Probability theory is applied to gain insight into the coexistence of background and sources through a probabilistic two-component mixture model, which provides consistent uncertainties of background and sources. A multi-resolution analysis is used for revealing faint and extended objects in the frame of the Bayesian mixture model. All the revealed sources are parameterized automatically providing source position, net counts, morphological parameters and their errors.
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decomposition of multicomponent mass spectra using Bayesian Probability theory
Journal of Mass Spectrometry, 2002Co-Authors: H D Kang, R Preuss, T Schwarzselinger, V DoseAbstract:We present a method for the decomposition of the mass spectra of mixed gases using Bayesian Probability theory. The method works without any calibration measurement and therefore applies also to the analysis of spectra containing unstable species. For the example of mixtures of three different hydrocarbon gases the algorithm provides concentrations and cracking coefficients of each mixture component and also their confidence intervals. The amount of information needed to obtain reliable results and its relation to the accuracy of our analysis are discussed. Copyright © 2002 John Wiley & Sons, Ltd.
Mark E. Borsuk - One of the best experts on this subject based on the ideXlab platform.
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2006Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures. (c) 2005 Elsevier B.V. All rights reserved
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2005Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures.
Peter Reichert - One of the best experts on this subject based on the ideXlab platform.
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2006Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures. (c) 2005 Elsevier B.V. All rights reserved
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2005Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures.
Armin Peter - One of the best experts on this subject based on the ideXlab platform.
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2006Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures. (c) 2005 Elsevier B.V. All rights reserved
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Assessing the decline of brown trout (Salmo trutta) in Swiss rivers using a Bayesian Probability network
Ecological Modelling, 2005Co-Authors: Mark E. Borsuk, Peter Reichert, Armin Peter, Eva Schager, Patricia Burkhardt-holmAbstract:A Bayesian Probability network has been developed to integrate the various scientific findings of an interdisciplinary research project on brown trout and their habitat in Switzerland. The network is based on a dynamic, age-structured population model, which is extended to include the effect of natural and anthropogenic influence factors. These include gravel bed conditions, water quality, disease rates, water temperature, habitat conditions, stocking practices, angler catch and flood frequency. Effect strength and associated uncertainty are described by conditional Probability distributions. These conditional probabilities were developed using experimental and field data, literature reports, and the elicited judgment of involved scientists. The model was applied to brown trout populations at 12 locations in four river basins. Model testing consisted of comparing predictions of juvenile and adult density under current conditions to the results of recent population surveys. The relative importance of the various influence factors was then assessed by comparing various model scenarios, including a hypothetical reference condition. A measure of causal strength was developed based on this comparison, and the major stress factors were analyzed according to this measure for each location. We found that suboptimal habitat conditions are the most important and ubiquitous stress factor and have impacts of sufficient magnitude to explain the reduced fish populations observed in recent years. However, other factors likely contribute to the declines, depending on local conditions. The model developed in this study can be used to provide these site-specific assessments and predict the effect of candidate management measures.
