SlidingBayes: exploring recombination using a sliding window approach based on Bayesian phylogenetic inference. by Paraskevis, D, Deforche, K, Lemey, P, Magiorkinis, G, Hatzakis, A, Vandamme, A

“…The tool can be used for nucleotide and amino acid sequences and combines all the modeling possibilities of MrBayes with the ability to plot the posterior probability support for clustering of various combinations of taxa.…”

Heuristic and Bayesian Tornado Prediction in Complex Terrain of Southern Wyoming by Thomas A. Andretta

“…Applying Bayes’ theorem to each case, there is a low to high (30 to 80%) posterior probability associated with vortex detection.…”
Get full text

Distributed simultaneous localization and mapping for mobile robot networks via hybrid dynamic belief propagation by Jiuqing Wan, Shaocong Bu, Jinsong Yu, Liping Zhong

“…The joint belief state of all robots and landmarks is encoded by a factor graph and the marginal posterior probability distribution of each variable is inferred by belief propagation. …”
Get full text

Motion correction and parameter estimation in dceMRI sequences: application to colorectal cancer. by Bhushan, M, Schnabel, J, Risser, L, Heinrich, M, Brady, J, Jenkinson, M

“…The similarity measure is based on the maximization of the joint posterior probability of the transformations which need to be applied to each image in the dataset to bring all images into alignment, and the physiological parameters which best explain the data. …”

Bayesian curve fitting using MCMC with applications to signal segmentation by Punskaya, E, Andrieu, C, Doucet, A, Fitzgerald, W

“…Hierarchical priors are developed and, as the resulting posterior probability distributions and Bayesian estimators do not admit closed-form analytical expressions, reversible jump Markov chain Monte Carlo (MCMC) methods are derived to estimate these quantities. …”

Measuring people’s covariational reasoning in Bayesian situations by Nicole Steib, Stefan Krauss, Karin Binder, Theresa Büchter, Katharina Böcherer-Linder, Andreas Eichler, Markus Vogel

“…Previous research on Bayesian reasoning has typically investigated people’s ability to assess a posterior probability (i.e., a positive predictive value) based on prior knowledge (i.e., base rate, true-positive rate, and false-positive rate). …”
Get full text

Measuring morbidity and mortality attributable to rheumatic heart disease in Fiji: a prototype study of disease burden in a developing country by data linkage by Tom Parks, Joseph Kado, Isimeli Tukana, Andrew Steer

“…Moreover, in sensitivity analyses, we found that changing posterior probability threshold above which record pairs were considered a match had limited impact on the results. …”
Get full text

Comparative analysis of the complete chloroplast genomes of six threatened subgenus Gynopodium (Magnolia) species by Huanhuan Xie, Lei Zhang, Cheng Zhang, Hong Chang, Zhenxiang Xi, Xiaoting Xu

“…Within section Gynopodium, M. sinica diverged first (posterior probability = 1, bootstrap = 100), followed by M. nitida, M. kachirachirai and M. lotungensis. …”
Get full text

Dynamic Bayesian Networks for Context-Aware Fall Risk Assessment by Gregory Koshmak, Maria Linden, Amy Loutfi

“…Each time step is represented by a single user activity and interacts with a fall sensors located on a mobile device. A posterior probability is calculated for each recognized activity or contextual information. …”
Get full text

Integrated probability of coronary heart disease subject to the −308 tumor necrosis factor-alpha SNP: a Bayesian meta-analysis by C. Sofia Carvalho

“…As a measure of correlation, we use the probability of occurrence of CHD conditional on the presence of the SNP, derived as the posterior probability of the Bayesian meta-analysis. The conditional probability indicates that CHD is not more likely to occur when the SNP is present, which suggests that the −308 TNF-alpha SNP is not implicated in the occurrence of CHD.…”
Get full text

Revisiting Strategies for Fitting Logistic Regression for Positive and Unlabeled Data by Wawrzeńczyk Adam, Mielniczuk Jan

“…We show by analyzing real data sets that, by using the DCCP to solve the optimization problem, we obtain significant improvements in the posterior probability and the label frequency estimation over the best available competitors.…”
Get full text

A Novel State Estimation Approach for Suspension System with Time-Varying and Unknown Noise Covariance by Qiangqiang Li, Zhiyong Chen, Wenku Shi

“…Using the VB approach, the time-varying noise covariance can be inferred from the inverse-Wishart distribution and then optimized state estimation by the finite sampling posterior probability distribution function (PDF) of noise covariance and backward Kalman smoothing. …”
Get full text

The phylogenomic position of the Critically Endangered Largetooth Sawfish Pristis pristis (Rhinopristiformes, Pristidae), inferred from the complete mitochondrial genome by Peter M. Kyne, Jun-Jie Wang, Dan Xiang, Xiao Chen, Pierre Feutry

“…Bayesian phylogenetic reconstruction showed that P. pristis clusters with the Pristis clade with strong posterior probability (100%).…”
Get full text

Uncertainty Modeling of a Modified SEIR Epidemic Model for COVID-19 by Yanjin Wang, Pei Wang, Shudao Zhang, Hao Pan

“…The uncertainty of the control basic reproduction number was studied with the posterior probability density function of the uncertainty model parameters. …”
Get full text

Bayesian analysis of trinomial data in behavioral experiments and its application to human studies of general anesthesia by Purdon, Patrick Lee, Brown, Emery N., Wong, Kin Foon Kevin, Smith, Anne C., Pierce, Eric T., Harrell, P. Grace, Walsh, John L., Salazar, Andres Felipe, Tavares, Casie L., Cimenser, Aylin, Prerau, Michael J., Mukamel, Eran A., Sampson, Aaron

“…We show applications of this approach to an example of responses to auditory stimuli at varying levels of propofol anesthesia ranging from light sedation to deep anesthesia in human subjects. The posterior probability densities of model parameters and the response probability are computed within a Bayesian framework using Markov Chain Monte Carlo methods.…”
Get full text
Get full text
Get full text

Stepwise fuzzy correction of the algorithm filters of random signals by A. A. Lobaty, A. S. Radkevich

“…At the same time, it is proposed to make a transition from the obtained probability characteristics of the estimated process to the membership functions of fuzzy logic based on the output filter parameters using the normalization of the posterior probability density. This approach allows to increase the accuracy of estimation, as it takes into account additional information and its complex processing.…”
Get full text

Individualization in Online Markets: A Generalized Model of Price Discrimination through Learning by Rasha Ahmed

“…I build a generalized model of price discrimination with Bayesian learning where a seller offers different bundles of the product that are sized and priced contingent on the posterior probability that the consumer is of a given type. …”
Get full text

Political-obsessed environment and investor sentiments: pricing liquidity through the microblogging behavioral perspective by Jawad Saleemi

“…The Bayesian theorem suggested a higher posterior probability for the occurrence of the liquidity-facilitating cost in response to the pessimistic sentiments. …”
Get full text

The complete mitochondrial genome of Oberthueria lunwan (Lepidoptera: Oberthuerinae) by Ming-Wei Liao, Xing-Shi Gu, Jin Xue, Xing Wang

“…The Oberthuerinae consisting of O. lunwan+Andraca theae was strongly supported as a monophyletic clade by the posterior probability of 1.00 and the bootstrap value of 100%, but the relationships among Oberthuerinae, Bombycidae, Satruniidae, and Sphingidae are need to be further confirmed.…”
Get full text

First report of Aphelenchoides bicaudatus (Imamura, 1931) Filipjev and Schuurmans Stekhoven, 1941 associated with grass in South Africa by Shokoohi E., Moyo N. A. G.

“…The obtained phylogenetic trees revealed a close positioning of the South African population to other representatives of A. bicaudatus with the maximum (1.00) posterior probability value. Principal component analysis (PCA) also indicated a variation within the populations of A. bicaudatus. …”
Get full text