Bayesian Model Averaging and Prior Sensitivity in Stochastic Frontier Analysis by Kamil Makieła, Błażej Mazur

“…We analyze sensitivity of different prior specifications on the aforementioned scale parameter with respect to posterior characteristics of technology, stochastic parameters, latent variables and—especially—the models’ posterior probabilities, which are crucial for adequate inference pooling. …”
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The complete mitochondrial genome of Lyssa zampa (Lepidoptera: Uraniidae) by Ge-Ge Yuan, Yuan-Wen Du, Lu Chen, Lang Ming, Gong Chen, Xing Wang

“…The results showed that the closest relationship between Uraniidae and Epicopeiidae was strongly supported by Bayesian posterior probabilities values of 0.99.…”
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Complete mitochondrial genome sequence of Macromia daimoji Okumura, 1949 (Odonata: Macromiidae) by Min Jee Kim, Su Yeon Jeong, Ah Rha Wang, Junghwa An, Iksoo Kim

“…Phylogenetic analyses using concatenated sequences of the 13 PCGs and 2 rRNA genes using the Bayesian inference (BI) method placed Macromiidae, represented by M. daimoji, as a sister group to Libellulidae with the highest nodal support [Bayesian posterior probabilities (BPP) = 1]. Unlike conventional phylogenetic analysis, the suborders Anisozygoptera and Zygoptera formed a strong sister group (BPP =1), justifying the use of different molecular markers for phylogenetic analysis.…”
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Exact Bayesian inference on discrete models via probability generating functions: a probabilistic programming approach by Zaiser, F, Murawski, AS, Ong, C-HL

“…<p>We present an exact Bayesian inference method for discrete statistical models, which can find exact solutions to a large class of discrete inference problems, even with infinite support and continuous priors.To express such models, we introduce a probabilistic programming language that supports discrete and continuous sampling, discrete observations, affine functions, (stochastic) branching, and conditioning on discrete events.Our key tool is <em>probability generating functions</em>:they provide a compact closed-form representation of distributions that are definable by programs, thus enabling the exact computation of posterior probabilities, expectation, variance, and higher moments.Our inference method is provably correct and fully automated in a tool called <em>Genfer</em>, which uses automatic differentiation (specifically, Taylor polynomials), but does not require computer algebra.Our experiments show that Genfer is often faster than the existing exact inference tools PSI, Dice, and Prodigy.On a range of real-world inference problems that none of these exact tools can solve, Genfer's performance is competitive with approximate Monte Carlo methods, while avoiding approximation errors.…”

Automatic Detection of Magnetic Disturbances in Magnetic Inertial Measurement Unit Sensors Based on Recurrent Neural Networks by Elkyn Alexander Belalcazar-Bolaños, Diego Torricelli, José L. Pons

“…The proposed approach considers magnetometer data as input to a long short-term memory (LSTM) neural network and obtains a labeled time series output with the posterior probabilities of magnetic disturbance. We trained our algorithm on a data set that reproduces a wide range of magnetic perturbations and MIMU motions in a repeatable and reproducible way. …”
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Bayesian and frequentist analysis of True and Error models by Michael H. Birnbaum

“…It is argued that less complex theories are not necessarily more likely to be true, and when the space of all possible theories is not well-defined, one should be cautious in interpreting calculated posterior probabilities that appear to prove a theory to be true.…”
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Recovering Crossed Random Effects in Mixed-Effects Models Using Model Averaging by José Ángel Martínez-Huertas, Ricardo Olmos

“…Specifically, we analyzed the bias and the root mean squared error (RMSE) of the estimations of the variances of random effects using model averaging with Akaike weights and Bayesian model averaging with BIC posterior probabilities, comparing them with two alternative analytical strategies as benchmarks: AIC and BIC model selection, and fitting a full random structure. …”
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The complete mitochondrial genome of the forest crested lizard, Calotes emma (Squamata, Agamidae) in China by the next generation sequencing by Wenfang Ma, Xiaotong Jing, Xiaomei Wei, Yong Huang

“…The phylogenetic tree recovered the monophyly of the Calotes and revealed that newly sequenced C. emma well supported as the sister taxon to C. mystaceus by very high posterior probabilities (1.0). The complete mitochondrial genome of C.emma in this study will be helpful for understanding the phylogenetic systematics and relationships, and molecular evolution of Calotes in Agamidae.…”
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Modelling Bottlenecks of Bike-Sharing Travel Using the Distinction between Endogenous and Exogenous Demand: A Case Study in Beijing by Sun Chao, Lu Jian

“…Based on a Bayesian network fault tree, we define the diagnosis mode of evidence nodes to calculate the posterior probabilities and to determine the most sensitive factors for bottlenecks. …”
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Molecular footprint of Frankliniella occidentalis from India: a vector of Tospoviruses by Devkant Singha, Vishal Kumar V, Rajasree Chakraborty, Shantanu Kundu, Arunkumar Hosamani, Vikas Kumar, Kaomud Tyagi

“…The phylogenetic analysis (NJ, ML, and BA) shows three distinct clades of F. occidentalis in the present dataset with high bootstrap supports and posterior probabilities. The K2P genetic distances further depicted high similarity of the generated sequences from India and Netherlands. …”
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BASiCS: Bayesian Analysis of Single-Cell Sequencing Data. by Catalina A Vallejos, John C Marioni, Sylvia Richardson

“…This is formalised by means of tail posterior probabilities associated to high (or low) biological cell-to-cell variance contributions, quantities that can be easily interpreted by users. …”
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Bayesian functional enrichment analysis for the Reactome database by Jing Cao

“…The inference on functional enrichment is then based on posterior probabilities that are immune to the size constraint. …”
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SOLUTION TO EVALUATION PROBLEM OF HIDDEN SEMI-MARKOV QP-MODELS by V. M. Deundyak, M. A. Zhdanova

“…This approach differs from the traditional one and employs posterior probabilities. The estimation problem solution of the hidden semi-Markov QP-model is an important step in solving the following more specific problem. …”
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The Risk Rating System for Noise-induced Hearing Loss in Korean Manufacturing Sites Based on the 2009 Survey on Work Environments by Young Sun Kim, Youn Ho Cho, Oh Jun Kwon, Seong Weon Choi, Kyung Yong Rhee

“…Methods: Through this analysis, a series of statistical models were built to determine posterior probabilities for each worksite with an aim to present risk ratings for noise levels at work. …”
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A Bayesian approach to optimizing cryopreservation protocols by Sammy Sambu

“…Secondly, using machine learning and generalized approaches via the Naïve Bayes Classification (NBC) method, these metadata were used to develop posterior probabilities for combinatorial approaches that were implicitly recorded in the metadata. …”
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Bayesian Variable Selection with Applications in Health Sciences by Gonzalo García-Donato, María Eugenia Castellanos, Alicia Quirós

“…In this paper, we introduce the basic concepts of the Bayesian approach for variable selection based on model choice, emphasizing the model space prior adoption and the algorithms for sampling from the model space and for posterior probabilities approximation; and show its application to two common problems in health sciences. …”
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Metaheuristic Algorithms Applied to Color Image Segmentation on HSV Space by Donatella Giuliani

“…Applying the Bayes’ rule, the posterior probabilities of the GMM can be used for assigning pixels to clusters. …”
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Complete mitochondrial genome of Scythris sinensis (Lepidoptera: Scythrididae) by Jeong Sun Park, Min Jee Kim, Sung-Soo Kim, Iksoo Kim

“…The nodal support for this sister relationship was the highest at Bayesian posterior probabilities = 1.…”
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Dated ancestral trees from binary trait data and their application to the diversification of languages by Nicholls, G, Gray, R

“…The reconstructed ages of tree nodes are relatively robust, whereas posterior probabilities for topology are not reliable. © 2008 Royal Statistical Society.…”

Complete mitochondrial genome of Asiagomphus coreanus (Odonata: Gomphidae), which is endemic to South Korea by Jeong Sun Park, Min Jee Kim, Sung Soo Kim, Iksoo Kim

“…Phylogenetic analyses using the concatenated sequences of 13 PCGs and two rRNA genes of the representative odonate mitogenomes by Bayesian inference method revealed that A. coreanus belongs to the Gomphidae family with a strong nodal support (Bayesian posterior probabilities = 1). Unlike previous phylogenetic analyses (with regards to suborder relationships) the suborder Anisozygoptera—which was represented by a single species, Epiophlebia superstes—was placed as the sister to Zygoptera.…”
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