Target allocation decision of incomplete information game based on Bayesian Nash equilibrium by WEI Na, LIU Mingyong

“…Then the types of AUVs to be allocated are selected, and the judgment on the types of the target assignment strategies adopted by the other party are modified through the posterior probability. An algorithm for solving incomplete information target assignment based on the multi-target discrete particle swarms is proposed, and the Bayesian Nash equilibrium target assignment strategies of the two sides are obtained, which provides strategic choice help for the commander's combat command.…”
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The complete mitochondrial genome of Dendrolimus kikuchii (Lepidoptera: Lasiocampidae) by Yu-Heng Wu, Xing-Shi Gu, Jin Xue, Xing Wang

“…The phylogenetic relationships among the lasiocampid species were (Trabala vishnou+ ((Apatelopteryx phenax+ Euthrix laeta) + (Dendrolimus kikuchii+ (D. spectabilis+ (D. tabulaeformis + D. punctatus))))), which were supported by a posterior probability of 1.00 and a high bootstrap value of 100%.…”
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The complete chloroplast genome of Sycopsis sinensis Oliver by Ye Peng, Limei Yang, Jing Wei

“…Phylogenetic analysis strongly shows that S. sinensis has a close relationship with Distylium macrophyllum, whose posterior probability is 1.0.…”
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Fault Diagnostics Based on the Analysis of Probability Distributions Estimated Using a Particle Filter by András Darányi, János Abonyi

“…The correlation structure of the posterior probability density can also be altered by failures. …”
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Analysis of Birefringence and Dispersion Effects from Spacetime-Symmetry Breaking in Gravitational Waves by Kellie O’Neal-Ault, Quentin G. Bailey, Tyann Dumerchat, Leïla Haegel, Jay Tasson

“…We discuss their implementation in the open-source LIGO-Virgo algorithm library suite, and we discuss the statistical method used to perform a Bayesian inference of the posterior probability of the coefficients for symmetry-breaking. …”
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Statistical inversion of normal‐mode interference spectral parameter in ocean waveguide by Wei Gao, Shuping Zhu, Xiaolei Li

“…Then, a misfit function is defined based on both the maximum singular value and the corresponding singular vector of the data matrix. Finally, the posterior probability distributions of unknown parameters are analysed based on statistical inversion theory. …”
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Data-based selection of creep constitutive models for high-Cr heat-resistant steel by Hitoshi Izuno, Masahiko Demura, Masaaki Tabuchi, Yoh-ichi Mototake, Masato Okada

“…The Bayesian free energy was significantly lower for the steady-state type under all the test conditions in the ranges of 50–90 MPa at 923 K, 90–160 MPa at 873 K and 170–240 MPa at 823 K, leading to the conclusion that the posterior probability was virtually 1.0. These findings mean that the experimental data supported the steady-state-type equation. …”
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A Multi-Model Diagnosis Method for Slowly Varying Faults of Plunger Pump by Changli Yu, Haodong Yan, Xingming Zhang, Hua Ye

“…In this article, to improve the performance of the multi-model fault diagnosis method, we combine the method and support vector machine and propose a new method by fusing the conditional probability of the multi-model with the posterior probability of the support vector machine. The experimental results on a marine plunger pump illustrate the effectiveness of the proposed method. …”
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Marvels and pitfalls of the Langevin algorithm in noisy high-dimensional inference by Sarao Mannelli, S, Biroli, G, Cammarota, C, Krzakala, F, Urbani, P, Zdeborová, L

“…We employ the Langevin algorithm to sample the posterior probability measure for the spiked mixed matrix-tensor model. …”

Morphological description and mitochondrial DNA-based phylogenetic placement of a new species of Callistoctopus Taki, 1964 (Cephalopoda, Octopodidae) from the southeast waters of... by Xiaodong Zheng, Chenxi Xu, Jiahua Li

“…Topologies resulting from ML and BI analyses of the COI gene showed a highly supported monophyletic clade (bootstrap value [BS] = 94%, posterior probability [PP] = 100%) containing all the specimens identified as C. xiaohongxu sp. nov.…”
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A New Measurement of Internet Addiction Using Diagnostic Classification Models by Dongbo Tu, Xuliang Gao, Daxun Wang, Yan Cai

“…More important, different from traditional questionnaires, the DCT-IA can simultaneously provide general-level diagnostic information and the detailed symptom criteria-level information about the posterior probability of satisfying each symptom criterion in DMS-5 for each patient, which gives insight into tailoring individual-specific treatments for internet addiction.…”
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The complete mitochondrial genome of Neoris haraldi Schawerda (Lepidoptera: Saturniidae) by Yu-Ying He, Xing Wang, Liu-Sheng Chen

“…The phylogenetic relationships among the saturniid species were (Neoris haraldi+ ((Attacus atlas+ (Samia cynthia + (Samia canningi + Samia ricini))) + ((Eriogyna pyretorum + Saturnia boisduvalii) + ((Actias artemis + Actias selene) + (Antheraea assama+ (Antheraea frithi + (Antheraea pernyi  Antheraea yamamai))))))), which was supported by a high bootstrap value of 100% and a posterior probability of 1.00.…”
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Entropy, Information, and the Updating of Probabilities by Ariel Caticha

“…The method of updating from a prior to posterior probability distribution is designed through an eliminative induction process. …”
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Parton distributions with scale uncertainties: a Monte Carlo sampling approach by Zahari Kassabov, Maria Ubiali, Cameron Voisey

“…A prior probability is assigned to each scale combinations set in the theoretical predictions used to obtain each PDF replica in the Monte Carlo ensemble and a posterior probability is obtained by selecting replicas that satisfy fit-quality criteria. …”
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Constrained Cubature Particle Filter for Vehicle Navigation by Li Xue, Yongmin Zhong, Yulan Han

“…Further, the convergence of the proposed CCPF is also rigorously proved, showing that the posterior probability function is converged when the particle number <i>N</i> → ∞. …”
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Belief Propagation and Revision in Networks with Loops by Weiss, Yair

“…For networks with a single loop, we derive ananalytical relationship between the steady state beliefs in the loopy network and the true posterior probability. Using this relationship we show a category of networks for which the MAP estimate obtained by belief update and by belief revision can be proven to be optimal (although the beliefs will be incorrect). …”
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Nonparametric Hamiltonian Monte Carlo by Mak, C, Zaiser, F, Ong, L

“…Probabilistic programming uses programs to express generative models whose posterior probability is then computed by built-in inference engines. …”

An empirical investigation into the role of subjective prior probability in searching for potentially missing items by Fanshawe, T

“…We show how the searcher's posterior probability that the target is present depends on the prior probability and the proportion of possible target locations already searched, and also consider the implications of imperfect search, when the probability of false-positive and false-negative decisions is non-zero. …”

Using temporally spaced sequences to simultaneously estimate migration rates, mutation rate and population sizes in measurably evolving populations by Ewing, G, Nicholls, G, Rodrigo, A

“…Markov chain Monte Carlo is used to collect samples from the posterior probability distribution. We demonstrate that this chain implementation successfully reaches equilibrium and recovers truth for simulated data. …”

A Bayesian nonparametric approach to testing for dependence between random variables by Filippi, S, Holmes, C

“…Pólya tree priors can accommodate known uncertainty in the form of the underlying sampling distribution and provides an explicit posterior probability measure of both depen- dence and independence. …”