Bayesian multiple changing-points detection by Sang Gil Kang, Woo Dong Lee, Yongku Kim

“…This study investigated the application of Bayesian multiple change-point detection techniques in the context of piecewise polynomial signals. …”
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Artificial intelligence-based Bayesian optimization and transformer model for tennis motion recognition by Shaowei Shi, Kun Huang

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Modeling the impact of climate change on corvus species distribution in Somaliland: Bayesian spatial point process approach for conservation by Abdisalam Hassan Muse, Maysaa Elmahi Abd Elwahab

“…This study provides a robust Bayesian spatial point process framework for conservation ecology, particularly where spatial patterns are prominent and data may be uncertain.…”
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BAYESIAN PARAMETER ESTIMATION OF WEIBULL DISTRIBUTION WITH MULTIPLE CHANGE POINTS FOR RANDOM CENSORING TEST MODEL WITH INCOMPLETE INFORMATION by HE ChaoBing

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Bayesian location of the QCD critical point: A holographic perspective by Ratti Claudia, Grefa Joaquin, Hippert Mauricio, Manning T. Andrew, Noronha Jorge, Noronha-Hostler Jacquelyn, Vazquez Israel Portillo, Rougemont Romulo, Trujillo Michael

“…We present a Bayesian analysis, based on holography and constrained by lattice QCD simulations, which leads to a prediction for the existence and location of the QCD critical point. …”
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Toward the Bayesian brain: a generative model of information transmission by vestibular sensory neurons by Michael G. Paulin, Kiri F. Pullar, Larry F. Hoffman

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Bayesian changepoint detection for epidemic models by Peter Johnson, Jesper Lund Pedersen

“…Abstract This paper demonstrates how Bayesian stochastic filtering techniques can be used to detect changepoints in the transmission rate, as well as identify the rate itself, in the spread of disease using the susceptible-infectious-recovered (SIR) model. …”
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Predictive models for overall health of hydroelectric equipment based on multi-measurement point output by Liu Dong, Kong Lijun, Song Jinghui, Zhou Yiming

“…Traditional health assessment methods often rely on single measurement point data, which has problems such as insufficient model precision and poor real-time performance. …”
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Modeling Chickenpox Dynamics with a Discrete Time Bayesian Stochastic Compartmental Model by A. Corberán-Vallet, F. J. Santonja, M. Jornet-Sanz, R.-J. Villanueva

“…As shown in a comparative study, these assumptions are fundamental to describe properly the evolution of the disease. The Bayesian analysis of the model allows us to calculate the posterior distribution of the model parameters and the posterior predictive distribution of chickenpox incidence, which facilitates the computation of point forecasts and prediction intervals.…”
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Bayesian adaptation in Poisson cognitive systems by Aleksander A. Solodov

“…From a mathematical point of view, such a model of cognitive functioning is the implementation of an adaptive Bayesian approach, which allows to reduce the influence of a priori distribution of an unknown quantity on its evaluation.The described model of the cognitive system is justified by the fact that the value of X is not only random, but also with an unknown a priori distribution, is not observed directly, and in some way must be evaluated by the cognitive system on the basis of the already existing in the unconscious number of events and the last event on the basis of which.The optimal estimation of the random parameter is used to solve the problem of classification of observations, i.e. the optimal verification of the one-sided hypothesis by the Bayesian criterion.As a result of the undertaken consideration the applicability of the developed formal definition of cognitive system for the formulation of various problems of analysis and synthesis of systems is demonstrated. …”
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Bayesian Disaggregate and Aggregate Calibration of Path Logit Choice Models by Giulio Erberto Cantarella, Antonino Vitetta

“…Calibration of choice models can be carried out from disaggregate vs. aggregate data, while inference statistical estimators can be specified through Bayesian vs. …”
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Application of Bayesian Vector Autoregressive Model in Regional Economic Forecast by Jinghao Ma, Yujie Shang, Hongyan Zhang

“…The Bayesian vector autoregressive (BVAR) model introduces the statistical properties of variables as the prior distribution of the parameters into the traditional vector autoregressive (VAR) model, which can overcome the problem of too little freedom. …”
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COMPARISON BETWEEN BAYESIAN QUANTILE REGRESSION AND BAYESIAN LASSO QUANTILE REGRESSION FOR MODELING POVERTY LINE WITH PRESENCE OF HETEROSCEDASTICITY IN WEST SUMATRA by Lilis Harianti Hasibuan, Ferra Yanuar, Dodi Devianto, Maiyastri Maiyastri, Rudiyanto Rudiyanto

“…The data used amounted to 133 data points from BPS in the years 2017 and 2023. Model parameters were estimated using MCMC with a Gibbs sampling approach. …”
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Modeling Spatial Data with Heteroscedasticity Using PLVCSAR Model: A Bayesian Quantile Regression Approach by Rongshang Chen, Zhiyong Chen

“…Spatial data not only enables smart cities to visualize, analyze, and interpret data related to location and space, but also helps departments make more informed decisions. We apply a Bayesian quantile regression (BQR) of the partially linear varying coefficient spatial autoregressive (PLVCSAR) model for spatial data to improve the prediction of performance. …”
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A Classical and Bayesian Approach for Parameter Estimation in Structural Equation Models by Naci Murat, Mehmet Ali Cengiz

“…The WinBUGS software package, which is freely available, can be used to implement Bayesian SEM analysis. Bayesian model fitting typically relies on MCMC, which involves simulating draws from the joint posterior distribution of the model unknowns (parameters and latent variables) through a computationally intensive procedure. …”
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3D Model Classification Based on Bayesian Classifier with AdaBoost by Xue-Yao Gao, Kai-Peng Li, Chun-Xiang Zhang, Bo Yu

“…Shape distribution descriptor expresses geometry relationship of random points on model surface and has rotation invariance. …”
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Bayesian Uncertainty Quantification of Reflooding Model With PSO–Kriging and PCA Approach by Ziyue Zhang, Dong Li, Nianfeng Wang, Meng Lei

“…In order to solve the problem of large time costs in sampling and inefficient use of transient sample points, particle swarm optimization (PSO)–Kriging model and principal component analysis (PCA) are adopted in this paper. …”
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Measurement noise covariance estimation in Gaussian filters: an online Bayesian solution by Gerald LaMountain, Jordi Vilà-Valls, Pau Closas

“…The proposed Bayesian statistics estimation (BSE) method is embedded within a sigma-point Kalman filter, and adapted to non-stationary noise processes using Gaussian filter consistency tests. …”
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Comparative evaluation of alternative Bayesian semi-parametric spatial crash frequency models by Gurdiljot Singh Gill, Wen Cheng, Mankirat Singh, Yihua Li

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Assessing the effect of model specification and prior sensitivity on Bayesian tests of temporal signal. by John H Tay, Arthur Kocher, Sebastian Duchene

“…BETS requires the specification of a full Bayesian phylogenetic model, posing several considerations for untangling the impact of model choice on the detection of temporal signal. …”
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