Bayesian model estimation and selection for epipolar geometry and generic manifold fitting by Torr, PHS

“…Third, a Bayesian model selection paradigm is proposed, the Bayesian formulation of the manifoldfitting problem uncovers an elegant solution to this problem, for which a new method ‘GRIC’ for approximating the posterior probability of each putative model is derived. …”

Bayesian inference of ancestral recombination graphs by Kelleher, JT, Mahmoudi, A, Koskela, J, Chan, Y-B, Balding, D

“…ARGinfer approximates posterior probability distributions for these and other quantities, providing interpretable assessments of uncertainty that we show to be well calibrated. …”

A Novel Cluster-Analysis Algorithm Based on MAP Framework for Multi-baseline InSAR Height Reconstruction by Si Qi, Wang Yu, Deng Yunkai, Li Ning, Zhang Heng

“…Finally, through the calculation of posterior probability to complete the reconstruction, an optimized method is adopted to improve the accuracy. …”
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Reliability evaluation of electromechanical braking system of mine hoist based on fault tree analysis and Bayesian network by Jin Huawei, Wang Xu, Xu Huwei, Chen Zhuqi

“…Firstly, the fault tree of the electro-mechanical braking system is established, and then the fault tree is transformed into a Bayesian network, and the posterior probability, probability importance and key importance of each root node are inversely deduced. …”
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Study on passive location method of shallow water acoustic source with single hydrophone by SHI Haijie, LI Jinghua, CHANG Hong

“…Histogram filtering method is used to solve the integral solution in the process of posterior probability estimation of sound source state. The hierarchical grid histogram filtering method is proposed for the first time, which effectively improves the efficiency of histogram filtering iterative algorithm. …”
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Systematic investigation of allelic regulatory activity of schizophrenia-associated common variants by Jessica C. McAfee, Sool Lee, Jiseok Lee, Jessica L. Bell, Oleh Krupa, Jessica Davis, Kimberly Insigne, Marielle L. Bond, Nanxiang Zhao, Alan P. Boyle, Douglas H. Phanstiel, Michael I. Love, Jason L. Stein, W. Brad Ruzicka, Jose Davila-Velderrain, Sriram Kosuri, Hyejung Won

“…Transcription factor binding had modest predictive power, while fine-map posterior probability, enhancer overlap, and evolutionary conservation failed to predict MPRA-positive variants. …”
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Optimal error regions for quantum state estimation by Jiangwei Shang, Hui Khoon Ng, Arun Sehrawat, Xikun Li, Berthold-Georg Englert

“…A related concept is the smallest credible region—the smallest region with pre-chosen posterior probability. In both cases, the optimal error region has constant likelihood on its boundary. …”
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Complete mitochondrial genome of the pumpkin fruit fly, Bactrocera depressa (Diptera: Tephritidae) by Su Yeon Jeong, Min Jee Kim, Jong Seok Kim, Iksoo Kim

“…Phylogenetic analysis using the 13 PCGs of Bactrocera species indicated that B. depressa is a sister to the sister group containing B. tau and B. cucurbitae with the highest nodal support (Bayesian posterior probability =1.0).…”
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Tucker tensor decomposition‐based tracking and Gaussian mixture model for anomaly localisation and detection in surveillance videos by Avinash Ratre, Vinod Pankajakshan

“…Finally, the features including shape and speed of the object are extracted that is used for classification using the GMM that follows the maximum posterior probability principle to detect and locate the anomaly in the video. …”
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Naïve and Semi-Naïve Bayesian Classification of Landslide Susceptibility Applied to the Kulekhani River Basin in Nepal as a Test Case by Florimond De Smedt, Prabin Kayastha, Megh Raj Dhital

“…The results show that the naïve Bayes approach with weights-of-evidence overpredicts the posterior probability of landslide occurrence by a factor of about two, while the semi-naïve Bayes approach, which uses logistic regression with weights-of-evidence, is unbiased and has more discriminatory power for landslide susceptibility mapping. …”
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Selectivity of timing: A meta-analysis of temporal processing in neuroimaging studies using activation likelihood estimation and reverse inference by Chloe Mondok, Martin Wiener

“…Results showed a constellation of regions that exhibited selective activation likelihood in explicit timing tasks with the largest posterior probability of activation resulting in the left supplementary motor area (SMA) and the bilateral insula. …”
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Comparative Study of Phillips Curve under Dual-stickiness Model Considering Heterogeneity in Iran\'s Economy by Maryam Hematy

“…To compare different pricing models in this study, four criteria have been applied: comparing the posterior probability of the models, comparing the moments of the simulated data of the model with real data, comparing the autocorrelation of the real inflation rate with the median of the posterior distribution of each of the models, and examining the impulse response functions. …”
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Hierarchical modeling of space-time dendroclimatic fields: Comparing a frequentist and a Bayesian approach by Michela Cameletti, Franco Biondi

“…BARCAST is developed in the Bayesian framework, and relies on Markov chain Monte Carlo (MCMC) algorithms for sampling values from posterior probability distributions of interest. STEM also explicitly includes covariates in the process model definition. …”
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Multisensor Estimation Fusion on Statistical Manifold by Xiangbing Chen, Jie Zhou

“…In the paper, we characterize local estimates from multiple distributed sensors as posterior probability densities, which are assumed to belong to a common parametric family. …”
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Estimation of ECHAM5 climate model closure parameters with adaptive MCMC by H. Järvinen, P. Räisänen, M. Laine, J. Tamminen, A. Ilin, E. Oja, A. Solonen, H. Haario

“…Here, parameter estimation, based on the adaptive Markov chain Monte Carlo (MCMC) method, is applied for estimation of joint posterior probability density of a small number (<i>n</i>=4) of closure parameters appearing in the ECHAM5 climate model. …”
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Assessment Of Portfolio Management Skills In Iranian Capital Market Mutual Funds:Baysian Model Averaging Approach by behrang asadi gharehjeloo, hossein abdo tabrizi

“…Eventually, using Bayesian Model Averaging approach, by implementing and evaluating numerous models consisting of variables used, posterior probability and probability of the inclusion of each variable in chosen models are presented.…”
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Graphical Local Genetic Algorithm for High-Dimensional Log-Linear Models by Lyndsay Roach, Xin Gao

“…We show that the graphical local genetic algorithm can be used successfully to fit non-decomposable models for both a low number of variables and a high number of variables. We use the posterior probability as a measure of fitness and parallel computing to decrease the computation time.…”
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Coupling a Neural Network-Based forward Model and a Bayesian Inversion Approach to Retrieve Wind Field from Spaceborne Polarimetric Radiometers by Luca Pulvirenti, Nazzareno Pierdicca, Frank S. Marzano

“…To retrieve wind speed, Minimum Variance (MV) and Maximum Posterior Probability (MAP) criteria have been used while, for wind direction, a Maximum Likelihood (ML) criterion has been exploited. …”
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Bimodal Extended Kalman Filter-Based Pedestrian Trajectory Prediction by Chien-Yu Lin, Lih-Jen Kau, Ching-Yao Chan

“…This prediction method estimates the prior probability of each parameter of the model through the dataset and updates the individual posterior probability of the pedestrian state through the bimodal extended Kalman filter. …”
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Gaussian discriminators between $$\varLambda $$ Λ CDM and wCDM cosmologies using expansion data by Ahmad Mehrabi, Jackson Levi Said

“…Abstract The Gaussian linear model provides a unique way to obtain the posterior probability distribution as well as the Bayesian evidence analytically. …”
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