Extracting more wisdom from the crowd by McCoy, John Patrick

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Morphing Banner Advertising by Urban, Glen L., MacDonald, Erin, Bordley, Robert, Hauser, John R., Liberali, Guilherme

“…Banners are matched to consumers based on posterior probabilities of latent segment membership, which are identified from consumers' clickstreams. …”
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A meta-cognitive learning algorithm for an extreme learning machine classifier by Suresh, Sundaram, Savitha, R., Kim, H. J.

“…The hinge-loss error function facilitates prediction of posterior probabilities better than the mean-square error and is hence preferred to develop the McELM classifier. …”
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Design and implementation of parallel bioinformatics algorithms on heterogeneous computing architectures by Liu, Yongchao.

“…MSAProbs is a new and practical multi-threaded aligner based on the pair hidden Markov models and partition function posterior probabilities for shared-memory CPUs. It achieves statistically significant alignment accuracy improvements over the existing top performing aligners, including ClustalW, MAFFT, MUSCLE, ProbCons, and Probalign, while demonstrating competitive speed. …”
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Characterization of the complete mitogenome data of Ischyja marapok (Lepidoptera: Noctuoidea: Erebidae) from Malaysia by Marylin Miga, Marylin Miga, Jahari, Puteri Nur Syahzanani, Sivachandran Parimannan, Sivachandran Parimannan, Heera Rajandas, Heera Rajandas, Abdul-Latiff, Muhammad Abu Bakar, Yap Jing Wei, Yap Jing Wei, Shamsir, Mohd Shahir, Mohd Salleh, Faezah

“…Phylogenetic tree analyses showed that the sequenced I. marapok resides within the Erebinae subfamily and is closely related to Ischyja manlia (MW664367) with high bootstrap support and posterior probabilities. This dataset presented the mitogenome data of I. marapok from Malaysia, which is valuable for further research of their phylogeny and the diversification of the Ischyja genus. …”
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Characterization of the complete mitogenome data of Ischyja marapok (Lepidoptera: Noctuoidea: Erebidae) from Malaysia by Marylin Miga, Marylin Miga, Jahari, Puteri Nur Syahzanani, Sivachandran Parimannan, Sivachandran Parimannan, Heera Rajandas, Heera Rajandas, Abdul-Latiff, Muhammad Abu Bakar, Yap Jing Wei, Yap Jing Wei, Shamsir, Mohd Shahir, Mohd Salleh, Faezah

“…Phylogenetic tree analyses showed that the sequenced I. marapok resides within the Erebinae subfamily and is closely related to Ischyja manlia (MW664367) with high bootstrap support and posterior probabilities. This dataset presented the mitogenome data of I. marapok from Malaysia, which is valuable for further research of their phylogeny and the diversification of the Ischyja genus. …”
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Characterization of the complete mitogenome data of Ischyja marapok (Lepidoptera: Noctuoidea: Erebidae) from Malaysia by Miga, Marylin, Jahari, Puteri Nur Syahzanani, Parimannan, Sivachandran, Rajandas, Heera, Abdul-Latiff, Muhammad Abu Bakar, Yap, Jing Wei, Shamsir, Mohd. Shahir, Mohd. Salleh, Faezah

“…Phylogenetic tree analyses showed that the sequenced I. marapok resides within the Erebinae subfamily and is closely related to Ischyja manlia (MW664367) with high bootstrap support and posterior probabilities. This dataset presented the mitogenome data of I. marapok from Malaysia, which is valuable for further research of their phylogeny and the diversification of the Ischyja genus. …”
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GWABLUP: genome-wide association assisted best linear unbiased prediction of genetic values by Theo Meuwissen, Leiv Sigbjorn Eikje, Arne B. Gjuvsland

“…Results GWABLUP consists of the following steps: (1) performing a GWAS in the training data which results in likelihood ratios; (2) smoothing the likelihood ratios over the SNPs; (3) combining the smoothed likelihood ratio with the prior probability of SNPs having non-zero effects, which yields the posterior probability of the SNPs; (4) calculating a weighted genomic relationship matrix using the posterior probabilities as weights; and (5) performing genomic prediction using the weighted genomic relationship matrix. …”
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SARS-CoV-2 Prevalence in Laparoscopic Surgery Filters. Analysis in Patients with Negative Oropharyngeal RT-qPCR in a Pandemic Context: A Cross-Sectional Study by Antoni Llueca, Manuela Barneo-Muñoz, Javier Escrig, Rosa de Llanos, on Behalf of COVID-Lap Working Group

“…From this estimation, the predictive posterior probabilities of finding <i>n</i> cases of negative oropharyngeal COVID-19 patients with positive filters increases with the increasing number of surgeries performed. …”
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Probabilistic prioritization of candidate pathway association with pathway score by Shu-Ju Lin, Tzu-Pin Lu, Qi-You Yu, Chuhsing Kate Hsiao

“…In the second stage, all scores are included simultaneously in a Bayesian logistic regression model which can evaluate the strength of association for each set and rank the sets based on posterior probabilities. Simulations from Gaussian distributions and human microarray data, and a breast cancer study with RNA-Seq are considered for demonstration and comparison with other existing methods. …”
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The mitochondrial genome of the mountain wooly tapir, Tapirus pinchaque and a formal test of the effect of altitude on the adaptive evolution of mitochondrial protein coding genes... by Edgar G. Gutiérrez, Jorge Ortega, Avery Savoie, J. Antonio Baeza

“…However, these results were supported by Likelihood Ratio Tests but not Bayesian Empirical Bayes posterior probabilities. Additional analyses (in DataMonkey) indicated a relaxation of selection strength in nad6, evidence of episodic diversifying selection in cob, and revealed episodic positive/diversifying selection signatures for two sites in nad1, and one site each in nad2 and nad4. …”
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Characterizing Clinical Heterogeneity in a Large Inpatient Addiction Treatment Sample: Confirmatory Latent Profile Analysis and Differential Levels of Craving and Impulsivity by Meenu Minhas, Alysha Cooper, Sarah Sousa, Mary Jean Costello, James MacKillop

“…Results: The CLPA confirmed the hypothesized 4-profile solution according to all indicators (eg, entropy = 0.90, all posterior probabilities ⩾.92). Profile 1 (n = 229 [32.2%], 24.9% female, median age in range of 45-49) reflected individuals with high alcohol severity and low psychiatric severity (HAlc/LPsy). …”
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Pronunciation Scoring With Goodness of Pronunciation and Dynamic Time Warping by Kavita Sheoran, Arpit Bajgoti, Rishik Gupta, Nishtha Jatana, Geetika Dhand, Charu Gupta, Pankaj Dadheech, Umar Yahya, Nagender Aneja

“…The current pronunciation scoring based on Goodness of Pronunciation (GOP) uses posterior probabilities of the Acoustic Models. Such algorithms suffer from generalization since they are utilized to determine a score metric for each phoneme rather than on the completeness or comparison with the ideal utterance of the words. …”
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SSVEP-Based Brain-Computer Interface Controlled Robotic Platform With Velocity Modulation by Yue Zhang, Kun Qian, Sheng Quan Xie, Chaoyang Shi, Jun Li, Zhi-Qiang Zhang

“…Subsequently, the Gaussian mixture model (GMM) and Bayesian inference were used to calculate the posterior probabilities that the signal came from a high or low brightness flicker. …”
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Bayesian Finite Element Model Updating and Assessment of Cable-Stayed Bridges Using Wireless Sensor Data by Parisa Asadollahi, Yong Huang, Jian Li

“…TMCMC is employed to draw samples from the posterior probability density function (PDF) of the structural model parameters and the uncertain prediction-error precision parameters if required. …”
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Hypononera ergatandria (Forel, 1893) – a cosmopolitan tramp species different from H. punctatissima (Roger, 1859) (Hymenoptera: Formicidae) by Bernhard Seifert

“…A linear discriminant analysis confirmed the results of these exploratory data analyses by 100 % and allocated each of the 27 type specimen to either cluster with posterior probabilities of p > 0.989. As junior synonyms of Hypoponera punctatissima(Roger, 1859) were established by type investigation: Hypoponera androgyna (Roger, 1859), Hypoponera tarda (Charsley, 1877), Hypoponera punctatissima r. jugata (Forel, 1892) and Hypoponera punctatissima var. exacta (Santschi, 1923). …”
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Building partnerships, capacity, and knowledge through a use of newly linked child development and education datasets in Ontario, Canada. by Magdalena Janus, Jeanne Sinclair, Jennifer Hove, Scott Davies

“…A 3-class solution was the best fit for a 20,000-person subsample of math trajectories based on AIC, BIC, ICL, and entropy values as well as sufficiently high proportions of posterior probabilities, which indicate confidence in class membership. 61% of sample showed steady moderate-high achievement; 9% started high, but declined, and 30% deteriorated then improved. …”
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Mini-FLOTAC as an alternative, non-invasive diagnostic tool for Schistosoma mansoni and other trematode infections in wildlife reservoirs by Catalano, S, Symeou, A, Marsh, K, Borlase, A, Léger, E, Fall, C, Sène, M, Diouf, N, Ianniello, D, Cringoli, G, Rinaldi, L, Bâ, K, Webster, J

“…A Bayesian model, applied to estimate the sensitivities of the two tests for the diagnosis of Schistosoma infections, indicated similar median posterior probabilities of 83.1% for Mini-FLOTAC technique and 82.9% for post-mortem examination (95% Bayesian credible intervals of 64.0-94.6% and 63.7-94.7%, respectively).…”

Interleukin-6 receptor antagonists in critically ill patients with Covid-19 by Gordon, AC, Mouncey, PR, Al‑Beidh, F, Estcourt, LJ

“…An analysis of 90-day survival showed improved survival in the pooled interleukin-6 receptor antagonist groups, yielding a hazard ratio for the comparison with the control group of 1.61 (95% credible interval, 1.25 to 2.08) and a posterior probability of superiority of more than 99.9%. …”

Lopinavir-ritonavir and hydroxychloroquine for critically ill patients with COVID-19: REMAP-CAP randomized controlled trial by Arabi, YM, Gordon, AC, Derde, LPG, Nichol, AD, Murthy, S, Beidh, FA, Annane, D, Swaidan, LA, Beane, A, Beasley, R, Berry, LR, Bhimani, Z, Bonten, MJM, Bradbury, CA, Brunkhorst, FM, Buxton, M, Buzgau, A, Cheng, A, De Jong, M, Detry, MA, Duffy, EJ, Estcourt, LJ, Fitzgerald, M, Fowler, R, Girard, TD, Goligher, EC, Goossens, H, Haniffa, R, Higgins, AM, Hills, TE, Horvat, CM, Huang, DT, King, AJ, Lamontagne, F, Lawler, PR, Lewis, R, Linstrum, K, Litton, E, Lorenzi, E, Malakouti, S, McAuley, DF, McGlothlin, A, Mcguinness, S, McVerry, BJ, Montgomery, SK, Morpeth, SC, Mouncey, PR, Orr, K, Parke, R, Parker, JC

“…The three interventions decreased organ support-free days compared to control (OR [95% credible interval]: 0.73 [0.55, 0.99], 0.57 [0.35, 0.83] 0.41 [0.24, 0.72]), yielding posterior probabilities that reached the threshold futility (≥ 99.0%), and high probabilities of harm (98.0%, 99.9% and > 99.9%, respectively). …”