Relating modularity maximization and stochastic block models in multilayer networks by Pamfil, AR, Howison, SD, Lambiotte, R, Porter, MA

“…E, 94 (2016), 052315], we show in multilayer networks that maximizing modularity is equivalent, under certain conditions, to maximizing the posterior probability of community assignments under suitably chosen stochastic block models. …”

Fine-mapping type 2 diabetes loci to single-variant resolution using high-density imputation and islet-specific epigenome maps by Mahajan, A, Taliun, D, Thurner, M, Robertson, N, Torres, J, Rayner, N, Payne, A, Bennett, A, Nylander, V, Lindgren, C, Marchini, J, Gloyn, A, Morris, A, McCarthy, M, al., E

“…With these data, we (i) extend the inventory of T2D-risk variants (243 loci, 135 newly implicated in T2D predisposition, comprising 403 distinct association signals); (ii) enrich discovery of lower-frequency risk alleles (80 index variants with minor allele frequency <5%, 14 with estimated allelic odds ratio >2); (iii) substantially improve fine-mapping of causal variants (at 51 signals, one variant accounted for >80% posterior probability of association (PPA)); (iv) extend fine-mapping through integration of tissue-specific epigenomic information (islet regulatory annotations extend the number of variants with PPA >80% to 73); (v) highlight validated therapeutic targets (18 genes with associations attributable to coding variants); and (vi) demonstrate enhanced potential for clinical translation (genome-wide chip heritability explains 18% of T2D risk; individuals in the extremes of a T2D polygenic risk score differ more than ninefold in prevalence).…”

The sensitivity of GPz estimates of photo-z posterior PDFs to realistically complex training set imperfections by Stylianou, N, Malz, AI, Hatfield, P, Crenshaw, JF, Gschwend, J

“…By evaluating the sensitivity of GPz to a range of increasingly severe imperfections, with a range of metrics (both of photo-z point estimates as well as posterior probability distribution functions, PDFs), we quantify the degree to which predictions get worse with higher degrees of degradation. …”

A framework for the detection of de novo mutations in family-based sequencing data by Francioli, L, Cretu-Stancu, M, Garimella, K, Fromer, M, Kloosterman, W, Genome Of The Netherlands Consortium, Samocha, K, Neale, B, Daly, M, Banks, E, Depristo, M, De Bakker, P, Palamara, P

“…Candidate de novo mutations (DNMs) are reported along with their posterior probability, providing a systematic way to prioritize them for validation. …”

Requirement Change Prediction Model for Small Software Systems by Rida Fatima, Furkh Zeshan, Adnan Ahmad, Muhamamd Hamid, Imen Filali, Amel Ali Alhussan, Hanaa A. Abdallah

“…The proposed approach utilizes the variable elimination method to obtain the posterior probability of the revisions in the software requirement document. …”
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Theoretical characterization of uncertainty in high-dimensional linear classification by Lucas Clarté, Bruno Loureiro, Florent Krzakala, Lenka Zdeborová

“…Even if the model generating the data and labels is known, computing the intrinsic uncertainty after learning the model from a limited number of samples amounts to sampling the corresponding posterior probability measure. Such sampling is computationally challenging in high-dimensional problems and theoretical results on heuristic uncertainty estimators in high-dimensions are thus scarce. …”
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The complete mitochondrial genome of Monopis longella Walker, 1863 (Lepidoptera: Tineidae) by Su Yeon Jeong, Jeong Sun Park, Min Jee Kim, Sung- Soo Kim, Iksoo Kim

“…Tineidae, represented by three species including M. longella, formed a monophyletic group with high support (Bayesian posterior probability = 0.99). Within Tineoidea the sister relationship between Tineidae and Meessiidae was obtained with the highest support, leaving Psychidae occupying the basal lineage of the two families.…”
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Damage Diagnosis of Single-Layer Latticed Shell Based on Temperature-Induced Strain under Bayesian Framework by Jie Xu, Zhengyang Zhao, Qian Ma, Ming Liu, Giuseppe Lacidogna

“…Then, Markov Chain Monte Carlo is adopted to analyze the newly proposed diagnosis index, based on which the frequency distribution histogram for the posterior probability of the diagnosis index is obtained. …”
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Quantifying physical insights cooperatively with exhaustive search for Bayesian spectroscopy of X-ray photoelectron spectra by Hiroyuki Kumazoe, Kazunori Iwamitsu, Masaki Imamura, Kazutoshi Takahashi, Yoh-ichi Mototake, Masato Okada, Ichiro Akai

“…As a result, we have successfully decomposed XPS of one monolayer (1ML), two monolayers (2ML), and quasi-freestanding 2ML (qfs-2ML) graphene samples deposited on SiC substrates with the meV order precision of the binding energy, in which the posterior probability distributions of the binding energies were obtained distinguishably between the different components of buffer layer even though they are observed as hump and shoulder structures because of their overlapping.…”
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Reliability Analysis of High-Voltage Drive Motor Systems in Terms of the Polymorphic Bayesian Network by Weiguang Zheng, Haonan Jiang, Shande Li, Qiuxiang Ma

“…The polymorphic Bayesian network (BN) model can accurately depict the high-voltage drive motor system’s miscellaneous fault states and solve the top event’s probability in every state, also solving the system and drawing the consistent conclusion that the presence of abrasive particles, high-temperature gluing, moisture, and localized high temperatures are the system’s weak links by solving the critical importance, probabilistic importance, and posterior probability of the underlying event, which provides a theoretical reference for structure contrive optimization and fault diagnosis. …”
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Out-of-Distribution Detection Based on Feature Fusion in Neural Network Classifier Pre-Trained by PEDCC-Loss by Qiuyu Zhu, Guohui Zheng, Jiakang Shen, Rui Wang

“…The existing OOD detection algorithms based on the neural network normally use a single scoring function to detect out-of-distribution examples, which start from the posterior probability and do not fully utilize the information of the pre-trained model. …”
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Probabilistic record linkage of de-identified research datasets with discrepancies using diagnosis codes by Hejblum, Boris P., Weber, Griffin M., Liao, Katherine P., Palmer, Nathan P., Churchill, Susanne, Shadick, Nancy A., Szolovits, Peter, Murphy, Shawn N., Kohane, Isaac S., Cai, Tianxi

“…It relies on Bayesian modelling of binarized diagnosis codes, and provides a posterior probability of matching for each patient pair, while considering all the data at once. …”
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A SPECTRAL-SPATIAL AUGMENTED ACTIVE LEARNING METHOD FOR HYPERSPECTRAL IMAGE CLASSIFICATION by S. Falahatnejad, A. Karami

“…In the proposed method, the most informative samples are selected by a new query function combination of a posterior probability-based uncertainty and angle-based diversity criteria. …”
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Source Symbol Purging-Based Distributed Conditional Arithmetic Coding by Jingjian Li, Wei Wang, Hong Mo, Mengting Zhao, Jianhua Chen

“…An improved calculation method for the posterior probability is also proposed based on the purging feature, such that the decoder can utilize the correlation within the source sequence to improve the decoding performance. …”
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Risk Assessment on Transport Aircraft Exceeding Tire Speed Rating Based on Cloud Bayesian Network by QIAN Yu, LONG Tao

“…The exceeding tire speed rating during takeoff will seriously threaten the flight safety.In order to effectively evaluate the risk level of the exceeding tire speed rating during take-off of the transport aircraft,an evaluation model based on cloud model and Bayesian network is proposed.This research selects eight risk indexes,including rotation speed,total weight,low pressure rotor speed,rotation rate,rotation time,elevator control amount,wind component and total air temperature to build the risk index system for exceeding tire speed rating.Then,the cloud model based on heuristic Gaussian cloud transformation algorithm and forward Gaussian cloud algorithm is used to realize the soft classification of exceeding tire speed rating risk level and the discretization of the indexes,and the prior probability of the indexes is calculated.Moreover,the Bayesian network for exceeding tire speed rating risk is constructed,and based on the established network and nodes information,the posterior probability of the nodes is calculated and the main inducement of exceeding tire speed rating is obtained through network reverse diagnosis.Finally,the simulation experiment has been completed by using the actual operation data from the airlines,and the results show that the evaluation results are consistent with the actual situation,which verify the effectiveness of the model.Therefore,the research can provide theoretical basis for the exceeding tire speed rating analysis and civil aviation takeoff safety risk management.…”
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A probabilistic assessment of surface water-groundwater exchange flux at a PCE contaminated site using groundwater modelling by Nikolas Benavides Höglund, Charlotte Sparrenbom, Rui Hugman

“…Results show 1) SW-GW exchange fluxes are likely to be significantly larger than previously estimated, and 2) prior estimations of mass influx are located near the center of the posterior probability distribution. Based on this, we recommend decision makers to focus remediation action on specific segments of the stream.…”
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Large-scale simultaneous inference with applications to the detection of differential expression with. (with discussion) by Geoffrey J. Mclachlan, Kent Wang, Shu-Kay Ng

“…We consider a straightforward and easily implemented method for estimating the posterior probability that an individual gene is null. The problem can be expressed in a two-component mixture framework, using an empirical Bayes approach. …”
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Learning body part‐based pose lexicons for semantic action recognition by Lijuan Zhou, Tao Jiang

“…Action classification is finally formulated as the problem of finding the maximum posterior probability that a given multiple sequences of visual frames follow multiple sequences of semantic poses, subject to the most likely visual pose sequences and alignment sequences. …”
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PTree: pattern-based, stochastic search for maximum parsimony phylogenies by Ivan Gregor, Lars Steinbrück, Alice C. McHardy

“…Nowadays, with next generation sequencing technologies producing sets comprising thousands of sequences, robust identification of the tree topology, which is optimal according to standard criteria such as maximum parsimony, maximum likelihood or posterior probability, with phylogenetic inference methods is a computationally very demanding task. …”
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Uncertainty Quantification When Learning Dynamical Models and Solvers With Variational Methods by N. Lafon, R. Fablet, P. Naveau

“…It combines an Evidence Lower BOund variational cost to a trainable gradient‐based solver to infer the state posterior probability distribution function given observation data. …”
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