scholarly journals A Bayesian Method for the Extinction

2014 ◽  
Vol 10 (S306) ◽  
pp. 22-24
Author(s):  
Hai-Jun Tian ◽  
Chao Liu ◽  
Jing-Yao Hu ◽  
Yang Xu ◽  
Xue-Lei Chen
Keyword(s):  
Bayesian Method ◽  
Simulated Data ◽  
Observational Errors ◽  
Validation Tests

AbstractWe propose a Bayesian method to measure the total Galactic extinction parameters, RV and AV. Validation tests based on the simulated data indicate that the method can achieve the accuracy of around 0.01 mag. We apply this method to the SDSS BHB stars in the northern Galactic cap and find that the derived extinctions are highly consistent with those from Schlegel et al. (1998). It suggests that the Bayesian method is promising for the extinction estimation, even the reddening values are close to the observational errors.

scholarly journals An experimental test of a Bayesian method for inferring extinction with varying search efforts

2014 ◽  
Author(s):  
Christopher Clements ◽  
Tamsin Lee ◽  
Micheal A McCarthy
Keyword(s):  
Experimental Test ◽  
Bayesian Method ◽  
Simulated Data ◽  
Experimental System ◽  
Biodiversity Loss ◽  
Search Effort ◽  
Prior Expectation ◽  
Effective Conservation ◽  
Different Levels ◽  
Do So

Determining whether a species is extinct or extant is notoriously difficult, but is fundamental to both our understanding of biodiversity loss, and our ability to implement effective conservation measures. Many methods have been proposed in an attempt to infer quantitatively whether a species has gone extinct, with many seeking to do so by using sets of historic sighting events. Until recently, however, no methods have been proposed that explicitly take into account search effort (the proportion of a habitat searched when looking for a species), a key determinant of if/when historic sighting events have occurred. Here we present the first test of a recently proposed Bayesian approach for inferring the extinction status of a species from a set of historic sighting events where the search effort that has produced the sightings can be explicitly included in the calculation. We utilize data from a highly tractable experimental system, as well as simulated data, to test whether the method is robust to changing search efforts, and different levels of detectability of a species. We find that, whilst in general the method performs well, it is susceptible to both changes in search effort through time, as well as how detectable a species is. In addition, we show that the value of the prior expectation that the species is extant has a large impact on the accuracy of the methods, and that selecting correct priors is critical for accurate inference of extinction status.

scholarly journals Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

2020 ◽  
Author(s):  
Matthias Flor ◽  
Michael Weiβ ◽  
Thomas Selhorst ◽  
Christine Müller-Graf ◽  
Matthias Greiner
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Estimation Method ◽  
Simulated Data ◽  
Credible Interval ◽  
Interval Length ◽  
Point Estimate ◽  
Data Sets ◽  
Prevalence Estimation

Abstract Background: Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage. Methods: In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method. Results: Across all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advanteges of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE's truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion: The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

scholarly journals MULTILAYER PERCEPTRON ARTIFICIAL NEURAL NETWORKS: AN APPROACH FOR LEARNING THROUGH THE BAYESIAN FRAMEWORK

2021 ◽  
Vol 39 (1) ◽  
pp. 45
Author(s):  
Suellen Teixeira Zavadzki de PAULI ◽  
Mariana KLEINA ◽  
Wagner Hugo BONAT
Keyword(s):  
Neural Networks ◽  
Multilayer Perceptron ◽  
Bayesian Method ◽  
Simulated Data ◽  
Activation Functions ◽  
Mlp Neural Network ◽  
Network Learning ◽  
Learning Capacity ◽  
Input Layer ◽  
Hidden Layer

The machine learning area has recently gained prominence and articial neural networks are among the most popular techniques in this eld. Such techniques have the learning capacity that occurs during an iterative process of model tting. Multilayer perceptron (MLP) is one of the rst networks that emerged and, for thisarchitecture, backpropagation and its modications are widely used learning algorithms. In this article, the learning of the MLP neural network was approached from the Bayesian perspective by using Monte Carlo via Markov Chains (MCMC) simulations. The MLP architecture consists of the input, hidden and output layers. In the structure, there are several weights that connect each neuron in each layer. The input layer is composedof the covariates of the model. In the hidden layer there are activation functions. In the output layer, there are the result which is compared with the observed value and the loss function is calculated. We analyzed the network learning through simulated data of known weights in order to understand the estimation by the Bayesian method. Subsequently, we predicted the price of WTI oil and obtained a credibility interval for theforecasts. We provide an R implementation and the datasets as supplementary materials.

scholarly journals Comparison of Bayesian and frequentist methods for prevalence estimation under misclassification

2020 ◽  
Author(s):  
Matthias Flor ◽  
Michael Weiβ ◽  
Thomas Selhorst ◽  
Christine Müller-Graf ◽  
Matthias Greiner
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Estimation Method ◽  
Simulated Data ◽  
Credible Interval ◽  
Interval Length ◽  
Point Estimate ◽  
Data Sets ◽  
Prevalence Estimation

Abstract Background: Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from truncation of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage. Methods : In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) error distribution of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method.Results: Across all data sets, the Bayesian point estimate and the RGE produced similar error distributions with slight advanteges of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE's truncation problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion: The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

scholarly journals SUBSTRA: Supervised Bayesian Patient Stratification

2019 ◽  
Vol 35 (18) ◽  
pp. 3263-3272
Author(s):  
Sahand Khakabimamaghani ◽  
Yogeshwar D Kelkar ◽  
Bruno M Grande ◽  
Ryan D Morin ◽  
Martin Ester ◽  
...  
Keyword(s):  
Drug Response ◽  
Bayesian Method ◽  
State Of The Art ◽  
Transplant Rejection ◽  
Simulated Data ◽  
Predictive Performance ◽  
Response Prediction ◽  
Supplementary Information ◽  
Patient Stratification ◽  
Feature Importance

Abstract Motivation Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. Results We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with the supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection. Availability and implementation The R code of SUBSTRA is available at https://github.com/sahandk/SUBSTRA. Supplementary information Supplementary data are available at Bioinformatics online.

Estimating Polygenic Effects Using Markers of the Entire Genome

Genetics ◽  
2003 ◽  
Vol 163 (2) ◽  
pp. 789-801 ◽  
Author(s):  
Shizhong Xu
Keyword(s):  
Gamma Distribution ◽  
Bayesian Method ◽  
Genome Mapping ◽  
Simulated Data ◽  
Interval Mapping ◽  
Mapping Method ◽  
Genetic Effects ◽  
Bayesian Regression ◽  
Entire Genome ◽  
Gene Effects

AbstractMolecular markers have been used to map quantitative trait loci. However, they are rarely used to evaluate effects of chromosome segments of the entire genome. The original interval-mapping approach and various modified versions of it may have limited use in evaluating the genetic effects of the entire genome because they require evaluation of multiple models and model selection. Here we present a Bayesian regression method to simultaneously estimate genetic effects associated with markers of the entire genome. With the Bayesian method, we were able to handle situations in which the number of effects is even larger than the number of observations. The key to the success is that we allow each marker effect to have its own variance parameter, which in turn has its own prior distribution so that the variance can be estimated from the data. Under this hierarchical model, we were able to handle a large number of markers and most of the markers may have negligible effects. As a result, it is possible to evaluate the distribution of the marker effects. Using data from the North American Barley Genome Mapping Project in double-haploid barley, we found that the distribution of gene effects follows closely an L-shaped Gamma distribution, which is in contrast to the bell-shaped Gamma distribution when the gene effects were estimated from interval mapping. In addition, we show that the Bayesian method serves as an alternative or even better QTL mapping method because it produces clearer signals for QTL. Similar results were found from simulated data sets of F2 and backcross (BC) families.

scholarly journals Validation of a generic Bayesian method for prevalence estimation under misclassification

2020 ◽  
Author(s):  
Matthias Flor ◽  
Michael Weiβ ◽  
Thomas Selhorst ◽  
Christine Müller-Graf ◽  
Matthias Greiner
Keyword(s):  
Confidence Interval ◽  
Confidence Intervals ◽  
Bayesian Method ◽  
Estimation Method ◽  
Simulated Data ◽  
Credible Interval ◽  
Interval Length ◽  
Point Estimate ◽  
Data Sets ◽  
Prevalence Estimation

Abstract Background: Various methods exist for statistical inference about a prevalence that consider misclassifications due to an imperfect diagnostic test. However, traditional methods are known to suffer from censoring of the prevalence estimate and the confidence intervals constructed around the point estimate, as well as from under-performance of the confidence intervals' coverage. Methods: In this study, we used simulated data sets to validate a Bayesian prevalence estimation method and compare its performance to frequentist methods, i.e. the Rogan-Gladen estimate for prevalence, RGE, in combination with several methods of confidence interval construction. Our performance measures are (i) bias of the point estimate against the simulated true prevalence and (ii) coverage and length of the confidence interval, or credible interval in the case of the Bayesian method. Results: Across all data sets, the Bayesian point estimate and the RGE produced similar bias distributions with slight advanteges of the former over the latter. In addition, the Bayesian estimate did not suffer from the RGE's censoring problem at zero or unity. With respect to coverage performance of the confidence and credible intervals, all of the traditional frequentist methods exhibited strong under-coverage, whereas the Bayesian credible interval as well as a newly developed frequentist method by Lang and Reiczigel performed as desired, with the Bayesian method having a very slight advantage in terms of interval length. Conclusion: The Bayesian prevalence estimation method should be prefered over traditional frequentist methods. An acceptable alternative is to combine the Rogan-Gladen point estimate with the Lang-Reiczigel confidence interval.

scholarly journals Multipoint Mapping of Viability and Segregation Distorting Loci Using Molecular Markers

Genetics ◽  
2000 ◽  
Vol 155 (3) ◽  
pp. 1439-1447
Author(s):  
Claus Vogl ◽  
Shizhong Xu
Keyword(s):  
Molecular Markers ◽  
Bayesian Method ◽  
Simulated Data ◽  
Real Data ◽  
Variable Number ◽  
Crossing Experiments ◽  
Mendelian Segregation ◽  
Pine Trees ◽  
Bayesian Mapping ◽  
Line Crossing

Abstract In line-crossing experiments, deviations from Mendelian segregation ratios are usually observed for some markers. We hypothesize that these deviations are caused by one or more segregation-distorting loci (SDL) linked to the markers. We develop both a maximum-likelihood (ML) method and a Bayesian method to map SDL using molecular markers. The ML mapping is implemented via an EM algorithm and the Bayesian method is performed via the Markov chain Monte Carlo (MCMC). The Bayesian mapping is computationally more intensive than the ML mapping but can handle more complicated models such as multiple SDL and variable number of SDL. Both methods are applied to a set of simulated data and real data from a cross of two Scots pine trees.

scholarly journals An experimental test of a Bayesian method for inferring extinction with varying search efforts

2014 ◽  
Author(s):  
Christopher Clements ◽  
Tamsin Lee ◽  
Micheal A McCarthy
Keyword(s):  
Experimental Test ◽  
Bayesian Method ◽  
Simulated Data ◽  
Experimental System ◽  
Biodiversity Loss ◽  
Search Effort ◽  
Prior Expectation ◽  
Effective Conservation ◽  
Different Levels ◽  
Do So

Determining whether a species is extinct or extant is notoriously difficult, but is fundamental to both our understanding of biodiversity loss, and our ability to implement effective conservation measures. Many methods have been proposed in an attempt to infer quantitatively whether a species has gone extinct, with many seeking to do so by using sets of historic sighting events. Until recently, however, no methods have been proposed that explicitly take into account search effort (the proportion of a habitat searched when looking for a species), a key determinant of if/when historic sighting events have occurred. Here we present the first test of a recently proposed Bayesian approach for inferring the extinction status of a species from a set of historic sighting events where the search effort that has produced the sightings can be explicitly included in the calculation. We utilize data from a highly tractable experimental system, as well as simulated data, to test whether the method is robust to changing search efforts, and different levels of detectability of a species. We find that, whilst in general the method performs well, it is susceptible to both changes in search effort through time, as well as how detectable a species is. In addition, we show that the value of the prior expectation that the species is extant has a large impact on the accuracy of the methods, and that selecting correct priors is critical for accurate inference of extinction status.

scholarly journals SUBSTRA: Supervised Bayesian Patient Stratification

2019 ◽  
Author(s):  
Sahand Khakabimamaghani ◽  
Yogeshwar Kelkar ◽  
Bruno Grande ◽  
Ryan Morin ◽  
Martin Ester ◽  
...  
Keyword(s):  
Drug Response ◽  
Bayesian Method ◽  
State Of The Art ◽  
Transplant Rejection ◽  
Simulated Data ◽  
Predictive Performance ◽  
Response Prediction ◽  
Patient Stratification ◽  
Specific Transcript ◽  
Feature Importance

Patient stratification methods are key to the vision of precision medicine. Here, we consider transcriptional data to segment the patient population into subsets relevant to a given phenotype. Whereas most existing patient stratification methods focus either on predictive performance or interpretable features, we developed a method striking a balance between these two important goals. We introduce a Bayesian method called SUBSTRA that uses regularized biclustering to identify patient subtypes and interpretable subtype-specific transcript clusters. The method iteratively re-weights feature importance to optimize phenotype prediction performance by producing more phenotype-relevant patient subtypes. We investigate the performance of SUBSTRA in finding relevant features using simulated data and successfully benchmark it against state-of-the-art unsupervised stratification methods and supervised alternatives. Moreover, SUBSTRA achieves predictive performance competitive with supervised benchmark methods and provides interpretable transcriptional features in diverse biological settings, such as drug response prediction, cancer diagnosis, or kidney transplant rejection.

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