Bayes Test Research Articles

Pengaruh Variasi Lama Pengeringan Terhadap Karakteristik Mutu Produk Dendeng Ikan Lele Dumbo (Clarias Gariepinus) Setelah Penggorengan

The study aimed at analyzing the influence of drying time variations on quality characteristics of jerked dumbo catfhis (clarias gariepinus) after being fried. Non-factorial Completely Randomized Design test, it was continued by ANOVA test. if ANOVA test result stated that it had siqnificance and very significant influence, it would be continued with Duncen Test. The next step was to determine the chosen product using Bayes method. After the chosen product was determined, it was continued with proximate test and TPC. The test result was analyzed applying IBM SPSS Statistic v.16. This study consisted of 3 treatments with two repetitions (7 hours drying time (A1), 8 hours drying time (A2), and 9 hours drying time (A3). The best drying time, according to Bayes test, was A2 (8 hours ) by having sensory organoleptic on parameter of color for 8,36, aroma for 8,36 taste for 8,36, and texture for 8,2, Then the chosen product contemed 39,63% water, 1,39% dust, 37,42 % protein, 2,46% fat, and 29,28% carbohydrate and total plate count was 1.87x103 or 3,27.

The Bayesian approach for real-world implementation of plasma p-tau217 in tertiary care memory clinics in Thailand.

Plasma phosphorylated tau (p-tau)217 is a promising biomarker for Alzheimer's disease (AD) diagnosis, but its clinical implementation remains challenging. We propose a strategy based on Bayes' theorem and test it in real-life memory clinics. Memory clinic patients were evaluated by neurocognitive specialists for prespecified diagnosis and subsequently underwent blood collection for p-tau217, cerebrospinal fluid, or amyloid positron emission tomography. Using cross-validation, the Bayesian approach (pretest probability × individualized likelihood ratio) was compared to other models for AD diagnosis. The Bayesian strategy demonstrated an area under the receiver operating characteristic curve (AUC) of 0.98 (95% confidence interval [CI]: 0.96-1.0), significantly outperforming multivariable logistic regression (p-tau217, age, apolipoprotein E; AUC 0.95, p=0.024) and p-tau217 alone (AUC=0.94, p=0.007). When applying the two-threshold approach, the Bayesian strategy yielded an accuracy of 0.94 (95% CI: 0.88-1.0) without requiring confirmatory tests in 62.9% of the iterations. The Bayesian strategy offers an effective and flexible approach to address the limitations of plasma p-tau217 in clinical practice. Incorporating pretest probability into the interpretation of plasma phosphorylated tau (p-tau)217 improves the diagnostic performance significantly. The strategy could obviate the need for confirmatory testing in most of the patients. Plasma p-tau217 proves useful as a biomarker for Alzheimer's disease in low- and middle-income country such as Thailand.

Mutu Kimia Dan Organoleptik Pempek Ikan Lele Dengan Subtitusi Tepung Buah Mangrove (Rhizophora mucronata)

This research aims to analyze the effect of mangrove flour substitution on the quality of pempek made from catfish which includes organoleptic (hedonic) and chemical quality. This research consists of formulation, hedonic organoleptic characteristics, selected products and chemical characteristics of selected products. The treatment in this research was the substitution of mangrove flour for tapioca flour with concentrations of 10g, 15g, and 20g. This research was designed using Kruskal Wallis to obtain hedonic organoleptic data and analyzed using Nonparametric Tests K Independent Samples. The analysis results were continued with Duncan's further test. Determination of selected products is carried out using the Bayes test. The results showed that pempek made from tapioca flour with the substitution of 10g, 15g and 20g mangrove flour had a significant effect on all organoleptic (hedonic) parameters, except appearance. Based on the analysis of the selected products, it was found that the pempek product with 15 g of mangrove flour substituted had chemical characteristics, namely a protein content of 10.44%; water content 40.43%; fat content 20.72%; ash content 1.79%; and carbohydrate content 26.63%. Meanwhile, the chemical test results for control or without substitution of mangrove flour were protein content 7.77%, water 37.54%, fat 28.38%, ash 1.11% and carbohydrates 25.21%.

Nonparametric Bayes multiresolution testing for high-dimensional rare events

In a variety of application areas, there is interest in assessing evidence of differences in the intensity of event realizations between groups. For example, in cancer genomic studies collecting data on rare variants, the focus is on assessing whether and how the variant profile changes with the disease subtype. Motivated by this application, we develop multiresolution nonparametric Bayes tests for differential mutation rates across groups. The multiresolution approach yields fast and accurate detection of spatial clusters of rare variants, and our nonparametric Bayes framework provides great flexibility for modelling the intensities of rare variants. Some theoretical properties are also assessed, including weak consistency of our Dirichlet Process-Poisson-Gamma mixture over multiple resolutions. Simulation studies illustrate excellent small sample properties relative to competitors, and we apply the method to detect rare variants related to common variable immunodeficiency from whole exome sequencing data on 215 patients and over 60,027 control subjects.

Robust incorporation of historical information with known type I error rate inflation.

Bayesian clinical trials can benefit from available historical information through the specification of informative prior distributions. Concerns are however often raised about the potential for prior-data conflict and the impact of Bayes test decisions on frequentist operating characteristics, with particular attention being assigned to inflation of type I error (TIE) rates. This motivates the development of principled borrowing mechanisms, that strike a balance between frequentist and Bayesian decisions. Ideally, the trust assigned to historical information defines the degree of robustness to prior-data conflict one is willing to sacrifice. However, such relationship is often not directly available when explicitly considering inflation of TIE rates. We build on available literature relating frequentist and Bayesian test decisions, and investigate a rationale for inflation of TIE rate which explicitly and linearly relates the amount of borrowing and the amount of TIE rate inflation in one-arm studies. A novel dynamic borrowing mechanism tailored to hypothesis testing is additionally proposed. We show that, while dynamic borrowing prevents the possibility to obtain a simple closed-form TIE rate computation, an explicit upper bound can still be enforced. Connections with the robust mixture prior approach, particularly in relation to the choice of the mixture weight and robust component, are made. Simulations are performed to show the properties of the approach for normal and binomial outcomes, and an exemplary application is demonstrated in a casestudy.

Multinomial Optimization of Naïve Bayes Through the Implementation of Particle Swarm Optimization

Sentiment analysis is widely used in cases of text processing and comments. One of the case studies is about the analysis of a hotel review by the public. The method used in analyzing a sentiment from comments or reviews of a hotel is the Naïve Bayes Classifier. One that can be used is the Multinomial Naïve Bayes method. In improving the results of the accuracy of the method required an optimization method. There are many optimization methods that can be applied to algorithms in sentiment analysis case studies. One well-known method is Particle Swarm Optimization (PSO). This study aims to determine the effect of PSO optimization on the Multinomial Naïve Bayes algorithm in the case of sentiment analysis. From the results of optimization and model testing, the highest accuracy was obtained in the Multinomial Naïve Bayes test with PSO optimization as hyperparameter tunning and feature selection of 97%.

Conversion and fusion method of multi-source and different populations maintainability prior data

Maintainability is an important universal quality characteristic that reflects the convenience, speed and economy of weapon and equipment maintenance. Making full use of multi-source data to accurately verify the degree to which the developed equipment meets the maintainability requirements is an important basis for equipment identification and acceptance. To solve the low reliability of equipment maintainability verification results caused by inaccurate comprehensive prior distribution obtained by fusing multi-source and different populations' prior data, a method of data conversion and fusion is proposed. A data conversion model based on the mean value ratio of failure mode maintenance data is constructed. The conversion factor is defined according to objective data to convert the different populations' prior data to the same populations. Next, a comparison of the prior distribution fitting performance of Bayes bootstrap, bootstrap, and two improved sample-resampling methods to are used obtain the closest fitting distribution to the true distribution. By constructing a multi-source data fusion model based on improved KL divergence, a symmetrical KL divergence is constructed to describe the similarity between each prior distribution and the field distribution for the weighted fusion of multi-source prior distribution in addition to determining and testing the normal comprehensive prior distribution. The results show that the conversion and fusion method effectively converts the multi-source and different populations’ maintainability prior data and obtains an accurate, comprehensive prior distribution by fusion, laying the foundation for applying the Bayes test method to verify the quantitative index of equipment maintainability.

Identifying Genetic Signatures from Single-Cell RNA Sequencing Data by Matrix Imputation and Reduced Set Gene Clustering

In this current era, the identification of both known and novel cell types, the representation of cells, predicting cell fates, classifying various tumor types, and studying heterogeneity in various cells are the key areas of interest in the analysis of single-cell RNA sequencing (scRNA-seq) data. Due to the nature of the data, cluster identification in single-cell sequencing data with high dimensions presents several difficulties. In this paper, we introduce a new framework that combines various strategies such as imputed matrix, minimum redundancy maximum relevance (MRMR) feature selection, and shrinkage clustering to discover gene signatures from scRNA-seq data. Firstly, we conducted the pre-filtering of the “drop-out” value in the data focusing solely on imputing the identified “drop-out” values. Next, we applied the MRMR feature selection method to the imputed data and obtained the top 100 features based on the MRMR feature selection optimization scores for further downstream analysis. Thereafter, we employed shrinkage clustering on the selected feature matrix to identify the cell clusters using a global optimization approach. Finally, we applied the Limma-Voom R tool employing voom normalization and an empirical Bayes test to detect differentially expressed features with a false discovery rate (FDR) < 0.001. In addition, we performed the KEGG pathway and gene ontology enrichment analysis of the identified biomarkers using David 6.8 software. Furthermore, we conducted miRNA target detection for the top gene markers and performed miRNA target gene interaction network analysis using the Cytoscape online tool. Subsequently, we compared our detected 100 markers with our previously detected top 100 cluster-specified markers ranked by FDR of the latest published article and discovered three common markers; namely, Cyp2b10, Mt1, Alpi, along with 97 novel markers. In addition, the Gene Set Enrichment Analysis (GSEA) of both marker sets also yields similar outcomes. Apart from this, we performed another comparative study with another published method, demonstrating that our model detects more significant markers than that model. To assess the efficiency of our framework, we apply it to another dataset and identify 20 strongly significant up-regulated markers. Additionally, we perform a comparative study of different imputation methods and include an ablation study to prove that every key phase of our framework is essential and strongly recommended. In summary, our proposed integrated framework efficiently discovers differentially expressed stronger gene signatures as well as up-regulated markers in single-cell RNA sequencing data.

Chemical, Physical, and Hedonic Quality Characteristics of Fish Cereal from Toman Fish Albumin Extract (Channa micropeltes) as Functional Food

<p><em>The nutritional value of fish cereal was determined by adding toman fish extract. Fish cereal is expected to be a useful food source rich in nutrients such as carbohydrates, vitamins, and minerals but rich in protein and albumin. Toman fish cereal is expected to be popular with people of all ages as a breakfast menu and can be a source of nutrition and increase endurance. The research aimed to obtain the formulation of toman fish albumin extract (Channa micropeltes) in fish cereal products as a functional food. This research was carried out in four stages: (1) preparation of fish albumin extract, (2) preparation of fish cereal; (3) Physical characteristics (hedonic test), fracture strength, and chemical characteristics (moisture content, ash content, protein content, fat content, and carbohydrate content), crude fibre content, albumin content, (4) determination of the best treatment using the Bayes method. The treatment with the addition of toman fish albumin consisted of 5 treatments with two replications. The treatment was carried out namely A1 0%, A2 30%, A3 35%, A4 40%, and A5 45%. The results showed that the highest protein content was around 4.57–13.33% and the albumin content of fish cereal was around 4.86–6.61%. This showed that fish cereal can be a functional food in increasing the immune system. Based on the Bayes test, the selected toman fish cereal formula was formula A2, namely 30% albumin extract.</em></p>

Digital Forensic Investigates Sexual Harassment on Telegram using Naïve Bayes

The widespread use of Telegram in Indonesia has had both positive and negative effects. While the app offers strong security features, it has also become a platform for digital crimes, including sexual harassment. This study aims to address the need for effective forensic analysis and classification methods by employing the National Institute of Justice (NIJ) methodology and Naïve Bayes classifier to analyze conversations on Telegram. The research evaluates the performance of digital forensic tools and the effectiveness of the Naïve Bayes method in identifying instances of sexual harassment conversation. The data analyzed is about conversations on the telegram application that contain sexual harassment. Data collection involves extracting relevant conversations and subjecting them to forensic analysis using MOBIL edit Forensic Express and FTK Imager. Based on the test results, the naïve Bayes algorithm can be used to classify conversations into positive and negative about sexual harassment. The value obtained from naïve Bayes testing is the accuracy value of 91.3%, Precision 100%, and Recall 90%.

Penerapan Naïve Bayes pada Potensi Akademik Siswa SD Negeri 5 Singakerta

Student potential cannot only be measured based on the result of academic scores, and many things influence student academic determination. The purpose of this research is to prove that students' potential is influenced by many things, such as character, academic activity, socioeconomic status, and distance of residence. By using the naïve Bayes method and testing with the confusion matrix, it will give results for this research. The data is from V-grade students at SD Negeri 5 Singakerta, with 120 students assisted by the homeroom teacher. Based on the results of the tests that have been carried out using a data sample of 10 students and 1 data using the Naïve Bayes, it is obtained that students have academic potential, and the results with the confusion matrix are accuracy of 75%, precision of 81%, and recall of 89%. In this case, it can be concluded that the academic potential of students can not only be measured based on the results of the final grade, but many other factors have an effect, the application of the Naïve Bayes in students' academic potential is appropriate to use.

Effect of Shrimp Shell Flour Substitution on Croissant Liking Level

This research aims to determine the effect of different ratios of shrimp shell flour and wheat flour in making croissants on the level of panelist preference. This research was conducted at the Fishery Product Processing Laboratory, Faculty of Fisheries and Marine Sciences, Padjadjaran University in September 2022. The method used in the research is the experimental method with the treatment of four different ratios of the use of wheat flour and shrimp shell flour, namely 100% wheat flour, 2.5% shrimp shell flour: 97.5% wheat flour, 5%: 95% and 7.5%: 92.5% with 20 panelists as replicates. The parameters tested were appearance, aroma, texture, and taste. Data processing used Friedman test and Bayes test. Observations made were hedonic tests including appearance, aroma, texture, and taste as well as chemical tests in the form of proximate and calcium content tests. The results showed that croissants with a usage ratio of 5% shrimp shell flour: 95% wheat flour is the most preferred treatment by panelists with an average value of appearance 7.10; aroma 7.00; texture 6.70; and taste 7.40; calcium 289.591 mg/100g.

Hypotheses Testing in Nonergodic Fractional Ornstein-Uhlenbeck Models

We obtain explicit form of fine large deviation theorems for the log-likelihood ratio in testing models with fractional nonergodic Ornstein-Uhlenbeck processes with Hurst parameter more than half and get explicit rates of decrease of the error probabilities of Neyman-Pearson, Bayes and minimax tests.

Empirical Bayes Decision for a Generalized Exponential Distribution with Contaminated Data

The two-sided and one-sided empirical Bayes test (EBT) rules for the parameter of a generalized exponential distribution with contaminated data (errors in variables) are constructed by a deconvolution kernel method, respectively. Under the type of the supersmooth error distributions and the supersmooth errors with the error level can be controlled situations, the asymptotically optimal uniformly over a class of prior distributions and uniform rates of convergence of the corresponding regret for the proposed EBT rules are obtained with suitable conditions. The example study shows that the assumptions and conditions of the main results of this paper are satisfied easily by calculating.

Forward Selection Attribute Reduction Technique for Optimizing Naïve Bayes Performance in Sperm Fertility Prediction

The problem of infertility between husband and wife is an important issue that destroys family harmony, and many people still consider infertility or infertility a female problem. However, about 7% of men of childbearing age suffer from infertility. The biggest factor causing male infertility is sperm quality problems. Sperm analysis can be the best predictor of male fertility potential. Machine learning and data mining techniques can be used to automate disease diagnosis. This study aims to obtain a regular form classification model from sperm sample data of 100 volunteers. This classification model can be used to predict male fertility levels into 2 classes, namely normal and alter (decreased fertility). This study uses a fertility dataset obtained from the UCI Machine Learning Repository. Before the data mining process, data preprocessing is required. The classification process is carried out using Naive Bayes and attribute reduction techniques using forward selection to see the increase in the accuracy of Naive Bayes performance. The Naive Bayes test without attribute reduction has an accuracy of 85%, while attribute reduction with forward selection has an accuracy of 88% in predicting sperm fertility. Therefore, by using forward selection with Naive Bayes to reduce attributes in this study, this study was able to increase accuracy by 3% and can be used to help predict sperm fertility

FORTIFICATION OF PROTEIN HYDROLYZATE OF MUJAIR FISH (Tilapia mossambica) IN EYEK-ENYEK (Cassava crackers)

This study aims to determine the best-enriched formula for tilapia fish using an analysis of chemical and organoleptic characteristics. The research was carried out at the Chemical Laboratory of Pakuan University, Bogor from March to May 2021. The method used was an experimental method with six additional treatments of tilapia protein hydrolyzate, namely 0%, 2.5%, 5%, 7.5%, 10% and 12.5% with 20 semi-trained panelists as replicates. The observed variables consisted of the hydrolyzed protein yield of African catfish, the panelists' preference for color, texture, and taste of geeks, and a proximate test on water content, ash content and insoluble ash in acids, proteins, fats, and carbohydrates. The level of liking was calculated by Friedman Test, after which followed by Bayes Test to find out the best formula with the addition of tilapia fish protein hydrolyzate. The results showed that the addition of tilapia protein hydrolyzate in the research was still considered favorable, but the preferred treatment was the addition of 5% tilapia protein hydrolyzate, from some characteristics such as: better appearance, better texture, taste and aroma 7 (preferred), moisture 6.1% content, ash content 1.9%, acid insoluble ash content 0.17%, protein content 6.2%, and carbohydrates content 84.9%.

A pooled Bayes test of independence using restricted pooling model for contingency tables from small areas

A pooled Bayes test of independence using restricted pooling model for contingency tables from small areas

Bias correction for multiple covariate analysis using empirical bayesian estimation in mixed-effects models for longitudinal data

The naïve empirical Bayes method has been widely used as an ad hoc tool in fitting linear mixed-effect models, which is much computationally efficient than the maximum likelihood estimation method. However, the shrinkage effect of the empirical Bayes method causes bias in the estimates of the fixed effects. Bias-correction has been proposed for the mixed-effects model when only one covariate is present. In this paper, we derive the shrinkage factor of the empirical Bayes predictors of the random effects and the variance-covariance matrix of the corrected estimates when the model has more than one covariate. The empirical Bayes estimates and test statistics are then corrected using the derived factor. Theoretical derivations, simulation studies and a real data application demonstrate the validity of the proposed method in that the corrected estimates are unbiased and the corrected tests have correct p-values.

MAP and Bayes tests in sparse vectors detection

MAP and Bayes tests in sparse vectors detection

Bayes Lines Tool (BLT):aSQL-script foranalyzingdiagnostic test results with an application to SARS-CoV-2-testing.

The performance of diagnostic tests crucially depends onthe diseaseprevalence, test sensitivity, and test specificity. However, these quantities are often not well known when tests are performed outside defined routine labprocedureswhich make the rating of the test results somewhat problematic. A current example isthe masstesting taking place within the context of the world-wide SARS-CoV-2 crisis. Here, for the first time in history, laboratory test resultshavea dramatic impact on political decisions. Therefore, transparent, comprehensible, and reliable data is mandatory. It isin the nature of wetlab tests that their quality and outcome are influenced by multiple factors reducing their performance by handling procedures, underlying test protocols,and analytical reagents. These limitations in sensitivity and specificityhave tobetaken into accountwhen calculatingthe realtest results. As a resolution method, we have developed a Bayesian calculator, the Bayes Lines Tool (BLT), foranalyzingdisease prevalence, test sensitivity, test specificity, and, therefore, true positive, false positive, true negative,and false negative numbers from official test outcome reports. The calculator performs a simple SQL (Structured Query Language) query and can easily be implementedonany system supporting SQL. We providean example of influenza test results from California, USA, as well astwoexamples of SARS-CoV-2 test results from official government reports from The Netherlands andGermany-Bavaria, to illustrate the possible parameter space of prevalence, sensitivity, and specificity consistent with the observed data. Finally, wediscussthis tool's multiple applications, including its putative importance for informing policy decisions.