Upper Bound on the Bit Error Probability of Systematic Binary Linear Codes via Their Weight Spectra by Jia Liu, Mingyu Zhang, Chaoyong Wang, Rongjun Chen, Xiaofeng An, Yufei Wang

“…Numerical results show that the proposed bound on the bit error probability matches well with the maximum-likelihood (ML) decoding simulation approach especially in the high signal-to-noise ratio (SNR) region, which is better than the recently proposed Ma bound.…”
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Neural network decoder for near-term surface-code experiments by Boris M. Varbanov, Marc Serra-Peralta, David Byfield, Barbara M. Terhal

“…., Nature (London) 614, 676 (2023)10.1038/s41586-022-05434-1], the neural network decoder achieves logical error rates approximately 25% lower than minimum-weight perfect matching, approaching the performance of a maximum-likelihood decoder. To demonstrate the flexibility of this decoder, we incorporate the soft information available in the analog readout of transmon qubits and evaluate the performance of this decoder in simulation using a symmetric Gaussian-noise model. …”
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Application of Harmony Search to Design Storm Estimation from Probability Distribution Models by Sukmin Yoon, Changsam Jeong, Taesam Lee

“…Generally, estimated parameters for PDMs are provided based on the method of moments, probability weighted moments, and maximum likelihood (ML). The results using ML are more reliable than the other methods. …”
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Trajetórias de desmatamento e de uso do solo em uma região dendeícola na Amazônia oriental. by César Teixeira Donato de Araújo, Eraldo Aparecido Trondoli Matricardi, Lívia de Freitas Navegantes-Alves

“…Supervised classification and Landsat imagery acquired between 1985 and 2015 were used in this analysis, with the Maximum Likelihood algorithm use. The classes forest, secondary forest, exposed soil, farming, natural fields, oil palm and water were considered. …”
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Contribution to the Molecular Phylogeny of Anthribidae (Coleoptera: Curculioniodea) Inferred from COI Sequences by Ali Nafiz Ekiz, Polen Döngel

“…The phylogenetic trees were inferred by using Neighbor-Joining (NJ) and Maximum Likelihood (ML) methods. Both method provided quite similar evolutionary histories. …”
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Diversity of Rust Fungi with Special Emphasis on <i>Hyalopsora erlangensis</i> Causing Disease in <i>Cystopteris chinensis</i> by An Yu, Xia Zhao, Xiaohong Chen

“…Phylogenetic analysis, based on internal transcribed spacer (ITS) sequences and 28S rDNA gene fragments, further confirms its distinctiveness from other Hyalopsora species, supported by high maximum parsimony (MP), maximum likelihood (ML), and Bayesian inference (BI) bootstrap values. …”
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Molecular phylogeny and comparative chloroplast genome analysis of the type species Crucigenia quadrata by Ting Wang, Huan Feng, Huan Zhu, Bojian Zhong

“…The Bayesian and maximum likelihood (ML) phylogenetic trees support a monophyletic group of C. quadrata and Scenedesmaceae (Chlorophyceae) species. …”
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Reliability Estimation of Inverse Lomax Distribution Using Extreme Ranked Set Sampling by Amer Ibrahim Al-Omari, Amal S. Hassan, Naif Alotaibi, Mansour Shrahili, Heba F. Nagy

“…In this study, the estimation of R=P Y<X is investigated when the stress and strength random variables are independent inverse Lomax distribution. Using the maximum likelihood approach, we obtain the R estimator via simple random sample (SRS), ranked set sampling (RSS), and extreme ranked set sampling (ERSS) methods. …”
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Frequency Synchronization Algorithms for MIMO-OFDM Systems with Periodic Preambles by Jian Sun, Fudong Li, Cheng-Xiang Wang, Xuemin Hong, Dongfeng Yuan

“…This paper addresses the problem of frequency synchronization in multiple-input multiple-output (MIMO) orthogonal frequency-division multiplexing (OFDM) systems with periodic preambles. Two Maximum Likelihood Estimators (MLEs) and four low complexity Best Linear Unbiased Estimators (BLUEs) are proposed. …”
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Some Experiments in Extreme‐Value Statistical Modeling of Magnetic Superstorm Intensities by Jeffrey J. Love

“…Lognormal, upper limit lognormal, generalized Pareto, and generalized extreme‐value model distributions are fitted to the −Dstm data using a maximum‐likelihood algorithm. All four candidate models provide good representations of the data. …”
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Vision-Based Satellite Recognition and Pose Estimation Using Gaussian Process Regression by Haopeng Zhang, Cong Zhang, Zhiguo Jiang, Yuan Yao, Gang Meng

“…Assuming that the regression function mapping from the image (or feature) of the target satellite to its category or pose follows a Gaussian process (GP) properly parameterized by a mean function and a covariance function, the predictive equations can be easily obtained by a maximum-likelihood approach when training data are given. …”
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New Aquilariomyces and Mangifericomes species (Pleosporales, Ascomycota) from Aquilaria spp. in China by Tian-Ye Du, Samantha C. Karunarathna, Kevin D. Hyde, Somrudee Nilthong, Ausana Mapook, Dong-Qin Dai, Kunhiraman C. Rajeshkumar, Abdallah M. Elgorban, Li-Su Han, Hao-Han Wang, Saowaluck Tibpromma

“…Full descriptions, photo plates, and phylogenetic analyses (maximum likelihood and Bayesian inference analyses based on LSU, ITS, SSU, tef1-α, and rpb2 gene combinations) of the new species are provided, along with a comprehensive list of saprobic fungi associated with Aquilaria spp.…”
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Inference of a Susceptible–Infectious stochastic model by Giuseppina Albano, Virginia Giorno, Francisco Torres-Ruiz

“…Several simulation studies are conduced to test the procedure, these include the time homogeneous case, for which a comparison with the results obtained by applying the maximum likelihood estimation was made, and cases in which the intensity function were time dependent with particular attention to periodic cases. …”
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Tritrophic associations and identification key for European species of the genus Binodoxys (Mackauer) (Hymenoptera: Braconidae: Aphidiinae) by Lazarević Maja J., Žikić Vladimir A., Milenković Darija N., Tomanović Željko M.

“…Based on the available molecular data, we constructed a maximum likelihood tree for six European species. Several new hosts were identified for the first time, and the geographical distribution of one species was broadened. …”
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Nondata-aided error vector magnitude performance analysis over κ−μ shadowed fading channel by Fan YANG, Xiaoping ZENG, Haiwei MAO, Xin JIAN, Derong DU, Xiaoheng TAN, Yiwen GAO

“…The performance prediction of wireless system over κ−μ shadowed fading channels was a challenging problem of wireless communications,which affects transmission scheme design seriously.To solve this problem,a novel method of quantifying the κ−μ shadowed fading channels performance based on nondata-aided error vector magnitude (NDA-EVM) was proposed.NDA-EVM was considered as a new metric to evaluate the change of the channels.The unified model to calculate different modulation order of NDA-EVM was analytically derived by maximum likelihood criterion.Moreover,the relationship between the κ−μ distribution and the NDA-EVM was built by using the attenuation factor of the channel as intermediate variable.Thereafter,the lower bounds of the NDA-EVM over the κ−μ shadowed fading channels were formulated,which was also simplified for various typical channels.The theoretical analysis was taken,moreover,numerical results were also conducted to verify the effectiveness of the derived formulation.It shows that NDA-EVM estimation has the lest root mean square error than data-aided signal to noise ratio (DA-SNR) estimation and error vector magnitude (DA-EVM) estimation over the κ−μ shadowed fading channels.The derived lower bounds closely match the theoretical values,especially at low SNR.In addition,the lower bounds are negatively related to all of the parameters of the κ−μ shadowed fading channels,which make it sensitive to the change of the fading channels.…”
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Queue Length Estimation for Signalized Intersections under Partially Connected Vehicle Environment by Lu Wei, Jin-hong Li, Li-wen Xu, Lei Gao, Jian Yang

“…The hyperparameter estimation problem of the prior distribution is solved by the maximum likelihood estimation (MLE) method. To validate the proposed queue length estimation method, a simulation environment with partially connected vehicles is established using VISSIM and Python for data generating. …”
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Phylogenetic Relationships in the Miracle Berry Genus, <i>Synsepalum</i>, Sensu Lato, and Relatives (Sapotaceae) by Daniel Potter, Mark Uleh

“…Bayesian analyses and Maximum likelihood of nuclear internal transcribed spacer (<i>ITS</i>) and plastid (<i>trnH-psbA</i>) sequences were used to reconstruct the phylogeny of the two genera. …”
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A complete chloroplast genome of Sedum lushanense S. S. Lai 2004 (Crassulaceae: Crassuloideae) by En-Dian Yang, Tong-Jun Liang, Zi-Yi Lei, Jie Zhang, Xiao-Xing Zhou

“…It contained 84 coding gene sequences (CDS), 34 transfer RNA (tRNA) genes, and eight ribosomal RNA (rRNA) genes. A maximum likelihood phylogenetic analysis revealed a close relationship between S. lushanense and S. lineare. …”
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Reliability analysis of new jointly Type-II hybrid NH censored data and its modeling for three engineering cases by Maysaa Elmahi Abd Elwahab, Ahmed Elshahhat, Ohud A. Alqasem, Mazen Nassar

“…We consider two estimation approaches, maximum likelihood and Bayesian methods, to obtain point and interval estimates of the model parameters. …”
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Frequentist and Bayesian Approaches in Modeling and Prediction of Extreme Rainfall Series: A Case Study from Southern Highlands Region of Tanzania by Erick A. Kyojo, Silas S. Mirau, Sarah E. Osima, Verdiana G. Masanja

“…Three estimation methods–L-moments, maximum likelihood estimation (MLE), and Bayesian Markov chain Monte Carlo (MCMC)–were employed to estimate GEV parameters and future return levels. …”
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