Revisiting the transits of CoRoT-7b at a lower activity level by Barros, S, Almenara, J, Deleuil, M, Diaz, R, Csizmadia, S, Cabrera, J, Chaintreuil, S, Cameron, A, Hatzes, A, Haywood, R, Lanza, A, Aigrain, S, Alonso, R, Bordé, R, Bouchy, F, Deeg, H, Erikson, A, Fridlund, M, Grziwa, S, Gandolfi, D, Guillot, T, Guenther, E, Leger, A, Moutou, C, Ollivier, M

“…A difference is found in the posterior probability distribution of the transit parameters between the previous CoRoT run (LRa01) and the new run (LRa06). …”

A TNF region haplotype offers protection from typhoid fever in Vietnamese patients. by Dunstan, S, Nguyen, T, Rockett, K, Forton, J, Morris, A, Diakite, M, Mai, N, Le, T, House, D, Parry, C, Ha, V, Nguyen, T, Dougan, G, Tran, T, Kwiatowski, D, Farrar, J

“…Haplotype-based analysis of the tag SNPs provided positive evidence of association with typhoid (posterior probability 0.821). The analysis highlighted a low-risk cluster of haplotypes that each carry the minor allele of T1 or T7, but not both, and otherwise carry the combination of alleles *12122*1111 at T1-T11, further supporting the one associated signal hypothesis. …”

Assessment of phylogenetic sensitivity for reconstructing HIV-1 epidemiological relationships. by Beloukas, A, Magiorkinis, E, Magiorkinis, G, Zavitsanou, A, Karamitros, T, Hatzakis, A, Paraskevis, D

“…All clusters of sequences belonging to the target population were correctly reconstructed by NJ and Bayesian methods receiving high bootstrap and posterior probability (PP) support, respectively. On the other hand, TreePuzzle failed to reconstruct or provide significant support for several clusters; high puzzling step support was associated with the inclusion of control sequences from the same geographic area as the target population. …”

Bayesian geoacoustic parameters inversion for multi-layer seabed in shallow sea using underwater acoustic field by Yangyang Xue, Hanhao Zhu, Hanhao Zhu, Xiaohan Wang, Guangxue Zheng, Xu Liu, Jiahui Wang

“…The maximum a posterior (MAP) model and posterior probability distribution of each parameter were estimated using the optimized simulated annealing (OSA) and Metropolis-Hastings sampling (MHS) methods. …”
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The Novel Sensor Network Structure for Classification Processing Based on the Machine Learning Method of the ACGAN by Yuantao Chen, Jiajun Tao, Jin Wang, Xi Chen, Jingbo Xie, Jie Xiong, Kai Yang

“…Firstly, the real/fake discrimination of sensor samples in the network has been canceled at the output layer of the discriminative network and only the posterior probability estimation of the sample tag is outputted. …”
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Simultaneous Estimates of Star-cluster Age, Metallicity, Mass, and Extinction (SESAMME). I. Presenting an MCMC Approach to Spectral Stellar Population Fitting by Logan H. Jones, Svea Hernandez, Linda J. Smith, Bethan L. James, Alessandra Aloisi, Søren Larsen

“…SESAMME compares an input spectrum of a star cluster to a grid of stellar population models with an added nebular continuum component, using Markov Chain Monte Carlo methods to sample the posterior probability distribution in four dimensions: cluster age, stellar metallicity Z , reddening E ( B − V ), and a normalization parameter equivalent to a cluster mass. …”
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Exercise is the dominant factor affecting the development of teenagers' eyesight—Based on the Bayesian model averaging by Zhong-hui Liu, Meng-fei Zhao, Shuai Ma, Yin Li, Zhi-ying Sun, Lei Gao

“…And we used BMA to select the risk factors through the BMS package in R.ResultsThe exercise was the only factor that affected the eyesight of junior and senior middle schoolers by BMA, with the posterior probability of 0.9736 and 0.9762, but not for the primary students. …”
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Evidence Networks: simple losses for fast, amortized, neural Bayesian model comparison by Niall Jeffrey, Benjamin D Wandelt

“…Multiple real-world and synthetic examples illustrate that Evidence Networks are explicitly independent of dimensionality of the parameter space and scale mildly with the complexity of the posterior probability density function. This simple yet powerful approach has broad implications for model inference tasks. …”
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PCEP: Few-Shot Model-Based Source Camera Identification by Bo Wang, Fei Yu, Yanyan Ma, Haining Zhao, Jiayao Hou, Weiming Zheng

“…Subsequently, we use the prototype sets to retrain SVM classifiers, and take the posterior probability of each image sample belonging to each class as the final projection vector. …”
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Bayesian compositional regression with microbiome features via variational inference by Darren A. V. Scott, Ernest Benavente, Julian Libiseller-Egger, Dmitry Fedorov, Jody Phelan, Elena Ilina, Polina Tikhonova, Alexander Kudryavstev, Julia Galeeva, Taane Clark, Alex Lewin

“…A reversible jump Monte Carlo Markov chain guided by the data through univariate approximations of the variational posterior probability of inclusion, with proposal parameters informed by approximating variational densities via auxiliary parameters, is used to estimate intractable marginal expectations. …”
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Large eQTL meta-analysis reveals differing patterns between cerebral cortical and cerebellar brain regions by Sieberts, Solveig K., Perumal, Thanneer M., Carrasquillo, Minerva M., Allen, Mariet, Reddy, Joseph S., Hoffman, Gabriel E., Dang, Kristen K., Calley, John, Ebert, Philip J., Eddy, James, Wang, Xue, Greenwood, Anna K., Mostafavi, Sara, Akbarian, Schahram, Bendl, Jaroslav, Breen, Michael S., Brennand, Kristen, Brown, Leanne, Browne, Andrew, Buxbaum, Joseph D., Charney, Alexander, Chess, Andrew, Couto, Lizette, Crawford, Greg, Devillers, Olivia, Devlin, Bernie, Dobbyn, Amanda, Domenici, Enrico, Filosi, Michele, Flatow, Elie, Francoeur, Nancy, Fullard, John, Gil, Sergio Espeso, Girdhar, Kiran, Gulyás-Kovács, Attila, Gur, Raquel, Hahn, Chang-Gyu, Haroutunian, Vahram, Hauberg, Mads Engel, Huckins, Laura, Jacobov, Rivky, Jiang, Yan, Johnson, Jessica S., Kassim, Bibi, Kim, Yungil, Klei, Lambertus, Kramer, Robin, Lauria, Mario, Lehner, Thomas, Lewis, David A., Lipska, Barbara K., Montgomery, Kelsey, Park, Royce, Rosenbluh, Chaggai, Roussos, Panagiotis, Ruderfer, Douglas M., Senthil, Geetha, Shah, Hardik R., Sloofman, Laura, Song, Lingyun, Stahl, Eli, Sullivan, Patrick, Visintainer, Roberto, Wang, Jiebiao, Wang, Ying-Chih, Wiseman, Jennifer, Xia, Eva, Zhang, Wen, Zharovsky, Elizabeth, Addis, Laura, Addo, Sadiya N., Airey, David Charles, Arnold, Matthias, Bennett, David A., Bi, Yingtao, Biber, Knut, Blach, Colette, Bradhsaw, Elizabeth, Brennan, Paul, Canet-Aviles, Rosa, Cao, Sherry, Cavalla, Anna, Chae, Yooree, Chen, William W., Cheng, Jie, Collier, David Andrew, Dage, Jeffrey L., Dammer, Eric B., Davis, Justin Wade, Davis, John, Drake, Derek, Duong, Duc, Eastwood, Brian J., Ehrlich, Michelle, Ellingson, Benjamin, Engelmann, Brett W., Esmaeelinieh, Sahar, Felsky, Daniel, Funk, Cory, Gaiteri, Chris, Gandy, Samuel, Gao, Fan, Gileadi, Opher, Golde, Todd, Grosskurth, Shaun E., Gupta, Rishi R., Gutteridge, Alex X., Haroutunian, Vahram, Hooli, Basavaraj, Humphryes-Kirilov, Neil, Iijima, Koichi, James, Corey, Jung, Paul M., Kaddurah-Daouk, Rima, Kastenmuller, Gabi, Klein, Hans-Ulrich, Kummer, Markus, Lacor, Pascale N., Lah, James, Laing, Emma, Levey, Allan, Li, Yupeng, Lipsky, Samantha, Liu, Yushi, Liu, Jimmy, Liu, Zhandong, Louie, Gregory, Lu, Tao, Ma, Yiyi, Matsuoka, Yasuji Y., Menon, Vilas, Miller, Bradley, Misko, Thomas P., Mollon, Jennifer E., Montgomery, Kelsey, Mukherjee, Sumit, Noggle, Scott, Pao, Ping-Chieh, Pearce, Tracy Young, Pearson, Neil, Penny, Michelle, Petyuk, Vladislav A., Price, Nathan, Quarless, Danjuma X., Ravikumar, Brinda, Ried, Janina S., Ruble, Cara Lee Ann, Runz, Heiko, Saykin, Andrew J., Schadt, Eric, Scherschel, James E., Seyfried, Nicholas, Shulman, Joshua M., Snyder, Phil, Soares, Holly, Srivastava, Gyan P., Stockmann, Henning, Taga, Mariko, Tasaki, Shinya, Tenenbaum, Jessie, Tsai, Li-Huei, Vasanthakumar, Aparna, Wachter, Astrid, Wang, Yaming, Wang, Hong, Wang, Minghui, Whelan, Christopher D., White, Charles, Woo, Kara H., Wren, Paul, Wu, Jessica W., Xi, Hualin S., Yankner, Bruce A., Younkin, Steven G., Yu, Lei, Zavodszky, Maria, Zhang, Wenling, Zhang, Guoqiang, Zhang, Bin, Zhu, Jun, Omberg, Larsson, Peters, Mette A., Logsdon, Benjamin A., De Jager, Philip L., Ertekin-Taner, Nilüfer, Mangravite, Lara M.

“…As a proof of principle for their utility, we apply a colocalization analysis to identify genes underlying the GWAS association peaks for schizophrenia and identify a potentially novel gene colocalization with lncRNA RP11-677M14.2 (posterior probability of colocalization 0.975).…”
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Diagnosing Metabolic Syndrome Using Genetically Optimised Bayesian ARTMAP by Kakudi, Habeebah Adamu, Loo, Chu Kiong, Moy, Foong Ming, Masuyama, Naoki, Pasupa, Kitsuchart

“…We use the GOBAM algorithm to classify individuals as either being at risk of MetS or not at risk of MetS with a related posterior probability, which ranges between 0 and 1. A data set of 11 237 Malaysians from the CLUSTer study stratified by age and gender into four subcategories was used to evaluate the proposed GOBAM algorithm. …”

Medical image segmentation using fuzzy c-mean (FCM), Bayesian method and user interaction by Balafar, Mohammad Ali, Ramli, Abdul Rahman, Saripan, M. Iqbal, Mashohor, Syamsiah

“…Each cluster is considered as a sub-class. Posterior probability of data to each sub class is calculated using data in those sub-classes. …”
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Taxonomic classification on phylogenic information appears a debatable approach: Lessons from the order Cypriniformes by Himanshu Priyadarshi, Rekha Das, Gowrimanohari Rakkannan

“…Tajima's D estimates of all studied genes indicated non-random evolution, violating the basic assumption of molecular phylogenetics. Bootstrap or Posterior probability values were also poor for several taxa indicating low robustness. …”
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Pathway-Activity Likelihood Analysis and Metabolite Annotation for Untargeted Metabolomics Using Probabilistic Modeling by Ramtin Hosseini, Neda Hassanpour, Li-Ping Liu, Soha Hassoun

“…Our approach captures metabolomics measurements and the biological network for the biological sample under study in a generative model and uses stochastic sampling to compute posterior probability distributions. PUMA predicts the likelihood of pathways being active, and then derives probabilistic annotations, which assign chemical identities to measurements. …”
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A platform trial approach to proof-of-concept (POC) studies in autism spectrum disorder: Autism spectrum POC initiative (ASPI) by J. Kyle Wathen, Shyla Jagannatha, Seth Ness, Abigail Bangerter, Gahan Pandina

“…The success and futility criteria for treatments are based on a Bayesian posterior probability model. Results: Overall, simulation results show the potential gain in terms of statistical properties especially for improved decision-making ability, while careful planning is needed due to the complexities of a platform trial. …”
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The structure of near misses and occupational accidents in the polish construction industry by Zuzanna Woźniak, Bożena Hoła

“…The power of each set and subset of events was then calculated. The posterior probability of the occurrence of events classified into individual sets was estimated using Bayes' theorem. …”
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Comparison Between Bayesian and Maximum Entropy Analyses of Flow Networks† by Steven H. Waldrip, Robert K. Niven

“…Both methods of inference update a prior to a posterior probability density function (pdf) by the inclusion of new information, in the form of data or constraints. …”
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Genome-Wide Association Study for Fatty Acid Composition in American Angus Cattle by Muhammad Dawood, Luke Matthew Kramer, Muhammad Imran Shabbir, James Mark Reecy

“…Thirty-six 1-Mb genomic regions with a posterior probability of inclusion (PPI) greater than 0.90 were identified to be associated with variation in the content of at least one fatty acid. …”
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Joint Tracking of Source and Environment Using Improved Particle Filtering in Shallow Water by Miao Dai, Yaan Li, Jinying Ye, Kunde Yang

“…The results show that we were able to track the source and environmental parameters simultaneously, and the uncertainties were evaluated in the form of time-evolving posterior probability densities (PPDs). The performance comparison confirms that the improved PF is superior to the standard PF, as it can reduce the parameter uncertainties. …”
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