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21 articles for “Bayesian statistics”
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Incomplete Information and Expected Performance Characteristics of Heat Engines
Abstract: In this paper, a connection between Bayesian statistics and the optimal performance characteristics of thermodynamic heat cycles is proposed. In particular, the subjective ignorance about internal energy scales of the working medium, quantified as a prior distribution leads to an expected behaviour of heat cycles which shows some remarkable thermodynamic features. It is shown that efficiency at maximum work is closely related to Curzon-Ahlborn (CA) efficiency. This efficiency usually appears …
Published in Trends in Mechanical Engineering & Technology · Vol. 4, Issue 3, 2014 · pp. 17–24 Read article
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Using the R Software to Conduct Network Meta-Analysis on Diabetes Data” h
Abstract: A meta-analysis's goal is typically to quantify the total treatment effect and draw conclusions regarding the differences in effects between the two treatments. Meta-analysis is a quantitative method for combining the findings of several studies in the social and medical sciences.. A meta-analysis might be of three typical forms. Network-wide, pairwise, and multivariate meta-analyses are all possible. The integrated assessment of more than two treatments is generally made possible by …
Published in Research & Reviews : Journal of Statistics · Vol. 13, Issue 2, 2024 · pp. 57–76 Read article
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Reducing the Uncertainty of Marine Accidents Failure Rate in Harsh Environment; Using Bayesian Theory
Abstract: The interest in arctic activities is strong, but has not led to significant quantifying analysis over the fact of finding rescannable failure rate. To attract investors and promote the development of a marine industry, a clear concept of project risk is paramount; in particular, issues relating to human role in operation are critical. In the public domain, reliability of information is often scarce or inappropriate for such environment. Also, an …
Published in Journal of Industrial Safety Engineering · Vol. 3, Issue 2, 2016 · pp. 30–39 Read article
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Chi-Square Statistic and Principal Component Analysis Based Compressed Feature Selection Approach for Naïve Bayesian Classifier
Abstract: Many of the machine learning algorithms are based on an assumption of attribute independency and often used in domains where the assumption doesn’t hold true. Naïve Bayesian (NB) classifier makes assumption that all the features are conditionally independent given the class labels; In this paper, attribute dependencies were analyzed using Chi-Square test and the Principal Component Analysis (PCA) was carried out on the whole dataset to get a set of …
Published in Journal of Artificial Intelligence Research & Advances · Vol. 2, Issue 2, 2015 · pp. 16–23 Read article
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Statistical Modeling: New Trends and Applications
Abstract: Statistical modeling plays a crucial role in various fields, aiding in decision-making, prediction, and inference. This article presents a comprehensive review of recent literature to identify and discuss the emerging trends in statisticaL modeling. The methodology involved a systematic search of relevant databases using specific keywords and selection criteria. The review highlights the limitations of traditional statistical modeling techniques and emphasizes the need for advancements in the field. The findings …
Published in Research & Reviews : Journal of Statistics · Vol. 12, Issue 1, 2023 · pp. 39–43 Read article
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Bayesian Approach for Gender Differentiation by Finger Ridges Count
Abstract: AbstractClassification of gender from fingerprints is an important step in forensic anthropology in order to identify the gender of a person. Fingerprints are considered to be the most reliable criteria for personal identification. The present analysis was undertaken to observe the distribution of finger print pattern in males and females from U.P. The objective of the present study was to study the differences in the finger loop ridge count among …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 2, 2013 · pp. 12–15 Read article
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Bayesian Weibull Model in Survival Analysis
Abstract: In this paper, we examine the performance of Maximum Likelihood Estimation (MLE) and Bayesian estimation of survival function of Weibull distribution with two parameters to the assigning informative prior and with three loss functions, namely, Linear Exponential (LINEX) Loss, General Entropy Loss and Squared Error Loss. Here the simulation study is carried out and the statistical analysis is done. Keywords: Weibull distribution, maximum likelihood estimation, Bayesian estimation, linear exponential loss, …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 24s–29s Read article
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Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are …
Published in Recent Trends in Electronics Communication Systems · pp. 1–6 Read article
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Blind Image Quality Assessment using NSS Approach in the DCT Domain
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.This algorithm does computation for the parameters which user expect at output. The certain extracted features approach depends on a simple Bayesian inference model to dipict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The resultant parameters of the model are used …
Published in Recent Trends in Electronics Communication Systems · Vol. 8, Issue 3, 2021 · pp. 1–6 Read article
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Bayesian Approximation Techniques of Inverse Rayleigh Distribution
Abstract: The present study is concerned with the estimation of inverse Rayleighdistribution using various Bayesian approximation techniques like normal approximation, and Tierney and Kadane (T-K) approximation. Different informative and non-informative priors are used to obtain the Baye’s estimate of inverse Rayleighdistribution under different approximation techniques. The simulation study has been conducted for comparison of Baye’s estimates obtained under different approximations using different priors. A real life example has also been discussed …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 50–59 Read article
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Automated Machine Learning System for Model Selection and Hyperparameter Optimization
Abstract: The proliferation of machine learning applications in various scientific and industrial domains has given rise to an urgent need for developing principled, automated techniques for optimal architecture selection and hyperparameter tuning for machine learning models without human expert intervention. In this paper, we introduce the Automated Machine Learning System for Model selection and hyperparameter Optimization (AMLSMO)—a state-of-the-art, all-encompassing AutoML system that combines the power of meta-learning-based warm-starting, Bayesian Optimization with …
Published in Recent Trends in Mathematics · Vol. 3, Issue 2, 2026 · pp. 15–23 Read article
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Blind Image Quality Assessment: An Overview
Abstract: We have develop an efficient model for improving image quality using IQA and NSS based on blind image Quality Assessment.. This algorithm does computation for the parameters which user expect at output. The certain extracted features approach relies on a simple Bayesian inference model to predict image quality scores. The project features are based on statistic scenes of discrete cosine transform for images. The estimated parameters of the model are …
Published in Journal of Electronic Design Technology · Vol. 12, Issue 3, 2021 · pp. 1–3 Read article
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Research Frame work of Missing Data Analysis using Mathematical Models
Abstract: AbstractThis article deals with providing an overall framework of missing data analysis using mathematical models. Mathematical models serves at large for missing data imputation. Several algorithms in machine learning techniques like Mean, Median, Standard Deviation, Regression and Naïve Bayesian classifier use Mathematical models for analysis. The performance of above mathematical models has been compared by using correlation statistics analysis gives the imputed values are positively related or negatively related or …
Published in Research & Reviews: Discrete Mathematical Structures · Vol. 7, Issue 3, 2020 · pp. 36–44 Read article
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Heteroscedasticity in Survey Data and Model Selection based on Weighted Schwarz-Bayesian Information Criterion
Abstract: AbstractThis paper proposed Weighted Schwarz Bayesian Information criterion for the purpose of selecting a best model from various competing models, when heteroscedasticity is present in the survey data. The authors found that the information loss between the true model and fitted models are equally weighted, instead of giving unequal weights. The computation of weights purely depends on the differential entropy of each sample observation and traditional Schwarz Bayesian information criterion …
Published in Research & Reviews : Journal of Statistics · Vol. 2, Issue 3, 2013 · pp. 8–23 Read article
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Bayesian Analysis of Operating Characteristic Curves of Single Sampling Plans by Attributes under Mixture of Two Binomial Distributions
Abstract: In the current scenario, manufacturing of products in single stream is not enough to meet the needs of consumers and in order to increase the production, two or more streams are required and the lots are formed from various streams. The production unit differs due to input raw materials, machineries, manpower and management, there may be inherent variation among the quality of the products manufactured from different streams. In this …
Published in Research & Reviews : Journal of Statistics · Vol. 7, Issue 1, 2018 · pp. 12s–23s Read article
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Comparison of Models of Machine Learning and Hyperparameter Optimization Methods on Various Datasets
Abstract: The most likely phase in achieving powerful and robust machine learning models is probably the hyperparameter tuning step. The traditional exhaustive methods of search (grid search and others) ensure that the search space is covered, but are computationally inexpensive; random search is less expensive and can still miss good regions; and lastly, the modern model-based and population-based methods (Bayesian optimization, tree-structured Parzen estimator (TPE), genetic algorithms) are thought to provide …
Published in Recent Trends in Programming languages · Vol. 13, Issue 1, 2026 · pp. 35–42 Read article
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Utilization of Statistical Learning Algorithms for Prediction of Elastic Modulus of Jointed Rock Mass
Abstract: This study uses two statistical learning algorithms for the prediction of elastic modulus (Ej) of jointed rockmass. The first algorithm uses support vector machine (SVM) that is firmly based on the theory of statisticallearning and uses regression technique by introducing -insensitive loss functionhas been adopted. Thesecond algorithm uses relevance vector machine (RVM). It is based on a Bayesian formulation of a linearmodel with an appropriate prior that results in a …
Published in Recent Trends in Civil Engineering & Technology · Vol. 1, Issue 1-3, 2011 · pp. 81–94 Read article
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Geospatial Depiction of Groundwater Level Disparities on a Temporal Component over Portions of Cuddapah District, India Using Contemporary Geo Tools and Interrelated Statistics
Abstract: This paper mainly focuses on comparing methods such as Deterministic methods, Geostatistical methods and Interpolation methods in order to obtain a good fit to represent groundwater level disparities existed in the study area from 1996 to 2015. Inverse Distance Weighting, Global Polynomial Interpolation, Radial Basis Functions, Local Polynomial Interpolation, Kriging, Empirical Bayesian Kriging, Kernel smoothing and Diffusion Kernel were the methods tested for the present study. The data set procured …
Published in Research & Reviews : Journal of Space Science & Technology · Vol. 6, Issue 2, 2017 · pp. 13–29 Read article
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Advance in Spatial Statistics for Environmental Data Analysis
Abstract: Recent advancements in spatial statistics have significantly enhanced the analysis of environmental data, addressing complex spatial dependencies and offering robust methodologies for environmental monitoring, modeling, and decision-making. This abstract highlights key developments in the field, emphasizing their applications and impact. Geostatistical Methods, Improved geostatistical techniques, such as kriging and co-kriging, provide more accurate predictions of environmental variables by incorporating spatial correlation structures. Advanced kriging variants, including Bayesian kriging and machine …
Published in Research & Reviews : Journal of Statistics · Vol. 12, Issue 3, 2023 · pp. 35–39 Read article
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Time Series Forecasting of Electricity Consumption: A Comparative Analysis of ARIMA and SARIMA Models
Abstract: Accurate electricity demand forecasting plays a vital role in energy planning, efficient power system operation, and sustainable resource management. This study conducts a comparative evaluation of the Autoregressive Integrated Moving Average (ARIMA) and Seasonal Autoregressive Integrated Moving Average (SARIMA) models using ten years of monthly electricity consumption data collected from a national electricity regulatory authority. The performance of both models is assessed using forecasting accuracy metrics, including Mean Absolute Error …
Published in Research & Reviews : Journal of Statistics · Vol. 15, Issue 2, 2026 · pp. 43–53 Read article