24 papers · ranked by Valyu relevance
Juan Sosa, Carlos Martínez, Cruz, Danna
This paper offers a comprehensive introduction to Bayesian inference, combining historical context, theoretical foundations, and core analytical examples. Beginning with Bayes' theorem and the philosophical distinctions between Bayesian and frequentist approaches, we develop the inferential framework for estimation…
Qin Gong, Ziwen Zhang, Lihua Zeng, Haiping Ren + 1 more
This paper proposes a new type of exponential-type Weibull distribution based on the inverse Weibull distribution --- the transformed inverse Weibull distribution. This distribution constructs a more flexible parameter structure through mathematical transformation and has a better fitting effect on actual data. We…
Paramahansa Pramanik, Arnab Kumar Maity, Anjan Mandal, Haley Kate Robinson
This study examines the application of Bayesian approach in the context of clinical trials, emphasizing their increasing importance in contemporary biomedical research. While conventional frequentist approach provides a foundational basis for analysis, it often lacks the flexibility to integrate prior knowledge, which…
Sooyong Lee, Soyoung Kim
Introduction This study compares Bayesian random coefficient prediction (BRCP) and Bayesian latent interaction (BINT) models to detect moderated mediation effects in multilevel contexts. Materials and methods We evaluated the performance of these models under various conditions using empirical data from the Trends in…
Oleg Stepanov, Alexey Isaev, Elena Dranitsyna, Yulia Litvinenko
A class of nonlinear filtering problems connected with data fusion from various navigation sensors and a navigation system is considered. A special feature of these problems is that the posterior probability density function (PDF) of the state vector being estimated changes its character from multi-extremal to…
Daniel K. Sewell, Alan T. Arakkal
Bayesian statistics is an integral part of contemporary applied science. bayesics provides a single framework, unified in syntax and output, for performing the most commonly used statistical procedures, ranging from one- and two-sample inference to general mediation analysis. bayesics leans hard away from the…
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The political, social and economic consequences of climate change drastically influence the requirements of modern energy systems and its components. This includes not only energy production but also concepts and innovations for its storage, especially in magnitudes of gigawatt hours. Carnot batteries, which convert…
Jörg Schultz, Liza Rothkoff, Edgar Aviles-Rosa, Nathaniel J. Hall + 1 more
Detection dogs play a critical role in operational settings ranging from explosives detection to medical diagnostics. Their unmatched olfactory capabilities allow them to locate trace-level targets that remain beyond the reach of current technology. However, detection performance can decline without overt behavioral…
Wenqisi Pan, Zeyu Lu, Wei Jiang, Johan Lim + 2 more
In meta-analyses of continuous outcomes, the sample mean and standard deviation (SD) are essential for synthesizing effect sizes across studies. However, clinical studies frequently report alternative summary statistics, such as the median, quartiles, and range. To enable inclusion of such studies, various methods have…
Jiawei Yan, Ju Liu, Weidong Liu, Jiyuan Tu
We propose a robust and scalable variational Bayes (VB) framework designed to effectively handle contamination and outliers in dataset. Our approach partitions the data into m disjoint subsets and formulates a joint optimization problem based on robust aggregation principles. A key insight is that the full posterior…
Nadav Ben Nun, Saharon Rosset, David Gresham, Yoav Ram
High-throughput experimental platforms now routinely generate data from dozens or hundreds of independent observations. Simulation-based inference (SBI) offers a powerful framework for estimating model parameters from such complex datasets, but standard methods struggle to scale to the noisy multiple-replicates regime…
Lanxi Zhang, Wenhao Gui, Zihan Zhao, Minghui Liu + 1 more
This study focuses on parameter estimation and reliability analysis for the two-parameter Rayleigh distribution under random censoring. It is shown that directly fitting the standard Rayleigh distribution can lead to substantial estimation errors, especially when the dataset contains a markedly high minimum value. To…
Yanqiu Zeng, Xinyu Wu, Shixiao Xiao, Boris Ryabko
This paper investigates statistical inference for the Shannon entropy of the Transmuted Weibull Distribution under progressively Type-II censored samples. The Transmuted Weibull Distribution is obtained by applying the quadratic rank transmutation map to the cumulative distribution function of the two-parameter Weibull…
van Leeuwen, Florian D., van Erp, Sara
Markov Chain Monte Carlo (MCMC) sampling is computationally expensive, especially for complex models. Alternative methods make simplifying assumptions about the posterior to reduce computational burden, but their impact on predictive performance remains unclear. This paper compares MCMC and non-MCMC methods for…
Yang Liu, Youjin Sung, Jonathan Williams, Jan Hannig
While Bayesian statistics is popular in psychological research for its intuitive uncertainty quantification and flexible decision-making, its performance in finite samples can be unreliable. In this paper, we demonstrate a key vulnerability: When analysts' chosen prior distribution mismatches the true…
Klaus Oberauer, Philipp Musfeld, Frederik Aust
Bayes factors often require numerical estimation because closed-form solutions are unavailable. In six simulation studies, we explored the reliability, bias, and computational cost of two easy-to-use and broadly applicable methods: bridge sampling and the Savage-Dickey density ratios, based on Gaussian, logspline, and…
Shaoyang Guo, Qian Sun, Xiaoyu Li
Introduction The No-U-Turn Sampler (NUTS), widely applied in psychometrics via the Stan platform, lacks algorithm-level systematic introduction for item response theory (IRT) models and tailored optimizations for specific models. This study systematically explicates the NUTS algorithm for the 4-parameter normal ogive…
Tommaso Costa
In this paper, I argue that the problem of induction dissolves when recast in terms of logical coherence (understood as internal consistency of credences under updating) rather than truth. Following E. T. Jaynes, probability is interpreted not as frequency or decision rule but as the extension of deductive logic to…
Francesco G. Rinaldi, Eugenio Piasini
To make sense of a noisy world, living beings constantly face decisions between competing interpretations for ambiguous sensory data. This process parallels statistical model selection, where most frameworks, like the Akaike Information Criterion (AIC) and the Bayesian Information Criterion (BIC), are based on a…
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Incorporating prior domain knowledge into Bayesian optimization (BO) remains difficult for statistical methods, which also typically suffer from limited interpretability. Large language models (LLMs) offer complementary strengths in reasoning and knowledge integration, but it remains unclear when and how they improve…
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Machine olfaction—the artificial replication of the sense of smell—faces significant challenges due to the absence of large, standardized training datasets. Unlike vision, language, and audio models, which benefit from extensive corpora such as ImageNet, GLUE, and AudioSet, olfaction lacks scaled equivalents and…
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Thorough treatment of conformation in computational chemistry is required to capture the subtle energy differences that lead to experimental observations. Accurate quantum chemistry calculations are very expensive and evaluation of the entire ensemble found during a conformational search is often unachievable. This is…
Luhe Li, Michael S. Landy
Sensory representations are inherently noisy, and monitoring this noise is essential for effective decision-making. This metacognitive ability of evaluating the quality of one’s perceptual decision is referred to as perceptual confidence. However, whether perceptual confidence accurately tracks internal noise remains…
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The rapid growth of worldwide computing power has transformed in silico chemistry into a discipline that is integrated into the daily work of many chemists. Nowadays, researchers find it increasingly straightforward to predict a wide range of molecular properties and chemi- cal processes at reasonable computational…