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Academic literature on the topic 'Baye factor'

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Published: 10 December 2022
Last updated: 28 January 2023

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Journal articles on the topic "Baye factor"

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Bertelsen, Thomas B., Asle Hoffart, Sondre Sverd Rekdal, and Rune Zahl-Olsen. "Bayes factor benefits for clinical psychology: review of child and adolescent evidence base." F1000Research 11 (September 23, 2022): 171. http://dx.doi.org/10.12688/f1000research.76842.2.

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Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice. Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors. Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors. Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials.
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Bertelsen, Thomas B., Asle Hoffart, Sondre Sverd Rekdal, and Rune Zahl-Olsen. "Bayes factor benefits for clinical psychology: review of child and adolescent evidence base." F1000Research 11 (February 11, 2022): 171. http://dx.doi.org/10.12688/f1000research.76842.1.

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Background: Statistical methods are a cornerstone of research in clinical psychology and are used in clinical trials and reviews to determine the best available evidence. The most widespread statistical framework, frequentist statistics, is often misunderstood and misused. Even when properly applied, this framework can lead to erroneous conclusions and unnecessarily prolonged trials. The implications for clinical psychology are difficulties in interpreting best available evidence and unnecessarily costly and burdensome research. An alternative framework, Bayesian statistics, is proposed as a solution to several issues with current practice. Methods: Statistical tests of primary outcome measures were extracted from 272 studies, which were cited in 11 recent reviews in the Evidence-based updates series in the Journal of Clinical Child and Adolescent Psychology. The extracted tests were examined regarding relevant features and re-analyzed using Bayes Factors. Results: When statistical tests were significant, the majority (98%) of re-analyzed tests agreed with such claims. When statistical tests were nonsignificant almost half (43%) of re-analyzed tests disagreed with such claims. Equally important for clinical research, an average of 13% fewer participants per study would have been required if the studies had used Bayes Factors. Conclusions: Bayes Factors offer benefits for research in clinical psychology through intuitive interpretations, and less costly trials.
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O'Hagan, Tony. "Bayes factors." Significance 3, no. 4 (November 28, 2006): 184–86. http://dx.doi.org/10.1111/j.1740-9713.2006.00204.x.

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Kass, Robert E., and Adrian E. Raftery. "Bayes Factors." Journal of the American Statistical Association 90, no. 430 (June 1995): 773–95. http://dx.doi.org/10.1080/01621459.1995.10476572.

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Ramos-Vera, Cristian Antony. "A case of replication using the Bayes factor in the health sciences." Revista Virtual de la Sociedad Paraguaya de Medicina Interna 8, no. 1 (March 30, 2021): 189–90. http://dx.doi.org/10.18004/rvspmi/2312-3893/2021.08.01.189.

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Reschenhofer, Erhard. "Approximating the Bayes factor." Statistics & Probability Letters 30, no. 3 (October 1996): 241–45. http://dx.doi.org/10.1016/0167-7152(95)00226-x.

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Aitkin, Murray. "Posterior Bayes Factors." Journal of the Royal Statistical Society: Series B (Methodological) 53, no. 1 (September 1991): 111–28. http://dx.doi.org/10.1111/j.2517-6161.1991.tb01812.x.

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Zhuo, Xiande, Ling Fan, Di Hu, and Huangda Zhu. "Multifactor optimization of MICP base on BP model." Journal of Physics: Conference Series 2200, no. 1 (February 1, 2022): 012003. http://dx.doi.org/10.1088/1742-6596/2200/1/012003.

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Abstract Microbial-induced calcite precipitation (MICP) can be used to cement soil and produce new biomaterials. The formation of this material is affected by many factors, such as physical, chemical, biological factors, many studies focused on the effect of a single factor on the strength ignoring the synergy between factors. Back-propagation neural network (BPNN) can be used as a multi-factor nonlinear prediction model and an analysis method. 140 MICP grout tests in the literature were summarized to15 factors affecting UCS and act as BPNN input data. At last, five key factors were elected based on weight analysis of well-trained BPNN. On this basis, a simple strength model composed of those factors is established, which can well predict the strength of MICP grouting soil with practical convenience. Key factors and strength prediction models help popularize MICP for engineering applications and optimize grouting experiments.
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Alarcón-Moyano, Jessica, and Silvia Matiacevich. "Development of bioactive/active additives based on essential oils: A review." Medicinal Plant Communications 3, no. 4 (November 30, 2020): 60–67. http://dx.doi.org/10.37360/mpc.20.3.4.12.

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The use of bioactive/active additives has been increasing in recent years, especially those derived from medicinal plants such as essential oils. However, due to essential oil oxidation it is necessary to protect it by encapsulation techniques such as: emulsion, spray- and/or freeze-drying as the most economical techniques. On the other hand, an important factor is to determine the appropriate wall material to obtain a prolonged or controlled release in the food or in the organism. Therefore, several factors affect the release of the compounds such as the type, amount of wall material and/or combination of wall materials. Therefore, the knowledge of all the aforementioned factors is important in order to make an adequate selection for the development of a bioactive/active additive based on essential oils.
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Iqbal, Zafar, Heung-No Lee, and Saeid Nooshabadi. "Highly Reliable Decision-Making Using Reliability Factor Feedback for Factory Condition Monitoring via WSNs." Wireless Communications and Mobile Computing 2018 (October 1, 2018): 1–9. http://dx.doi.org/10.1155/2018/8058624.

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Cooperation among sensors in a wireless sensor network, deployed for industrial monitoring in an indoor scenario, is a topic of interest in the smart factory and smart city research. The indoor wireless communication channel is very harsh and the observations of all the sensors cannot be sent reliably to the base station. Failure to transmit correct sensing results to the base station may result in false alarms or missed detection of events. Therefore, we propose a cooperation scheme for the wireless sensors to send the data reliably to the base station. Our aim is to increase the reliability of the received information, reduce the probability of error, lower the overall power consumption, and keep the latency to an acceptable low level. We propose a reliability factor feedback algorithm to adjust the weight of unreliable sensors in the decision-making process. The proposed scheme is analyzed based on its latency, power consumption, and packet delivery ratio. Our results show significant improvement in the reliability of the received data, improved packet delivery, and reduced false alarm ratio for full repetition and cluster head-based cooperation. The power consumption and latency in data transmission are also kept to an acceptable low level.
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Dissertations / Theses on the topic "Baye factor"

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Bhatta, Dilli. "A Bayesian Test of Independence for Two-way Contingency Tables Under Cluster Sampling." Digital WPI, 2013. https://digitalcommons.wpi.edu/etd-dissertations/128.

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We consider a Bayesian approach to the study of independence in a two-way contingency table obtained from a two-stage cluster sampling design. We study the association between two categorical variables when (a) there are no covariates and (b) there are covariates at both unit and cluster levels. Our main idea for the Bayesian test of independence is to convert the cluster sample into an equivalent simple random sample which provides a surrogate of the original sample. Then, this surrogate sample is used to compute the Bayes factor to make an inference about independence. For the test of independence without covariates, the Rao-Scott corrections to the standard chi-squared (or likelihood ratio) statistic were developed. They are ``large sample' methods and provide appropriate inference when there are large cell counts. However, they are less successful when there are small cell counts. We have developed the methodology to overcome the limitations of Rao-Scott correction. We have used a hierarchical Bayesian model to convert the observed cluster samples to simple random samples. This provides the surrogate samples which can be used to derive the distribution of the Bayes factor to make an inference about independence. We have used a sampling-based method to fit the model. For the test of independence with covariates, we first convert the cluster sample with covariates to a cluster sample without covariates. We use multinomial logistic regression model with random effects to accommodate the cluster effects. Our idea is to fit the cluster samples to the random effect models and predict the new samples by adjusting with the covariates. This provides the cluster sample without covariates. We then use a hierarchical Bayesian model to convert this cluster sample to a simple random sample which allows us to calculate the Bayes factor to make an inference about independence. We use Markov chain Monte Carlo methods to fit our models. We apply our first method to the Third International Mathematics and Science Study (1995) for third grade U.S. students in which we study the association between the mathematics test scores and the communities the students come from, and science test scores and the communities the students come from. We also provide a simulation study which establishes our methodology as a viable alternative to the Rao-Scott approximations for relatively small two-stage cluster samples. We apply our second method to the data from the Trend in International Mathematics and Science Study (2007) for fourth grade U.S. students to assess the association between the mathematics and science scores represented as categorical variables and also provide the simulation study. The result shows that if there is strong association between two categorical variables, there is no difference between the significance of the test in using the model (a) with covariates and (b) without covariates. However, in simulation studies, there is a noticeable difference in the significance of the test between the two models when there are borderline cases (i.e., situations where there is marginal significance).
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Wang, David I.-Chung. "Speaker diarization : "who spoke when"." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/59624/1/David_Wang_Thesis.pdf.

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Speaker diarization is the process of annotating an input audio with information that attributes temporal regions of the audio signal to their respective sources, which may include both speech and non-speech events. For speech regions, the diarization system also specifies the locations of speaker boundaries and assign relative speaker labels to each homogeneous segment of speech. In short, speaker diarization systems effectively answer the question of ‘who spoke when’. There are several important applications for speaker diarization technology, such as facilitating speaker indexing systems to allow users to directly access the relevant segments of interest within a given audio, and assisting with other downstream processes such as summarizing and parsing. When combined with automatic speech recognition (ASR) systems, the metadata extracted from a speaker diarization system can provide complementary information for ASR transcripts including the location of speaker turns and relative speaker segment labels, making the transcripts more readable. Speaker diarization output can also be used to localize the instances of specific speakers to pool data for model adaptation, which in turn boosts transcription accuracies. Speaker diarization therefore plays an important role as a preliminary step in automatic transcription of audio data. The aim of this work is to improve the usefulness and practicality of speaker diarization technology, through the reduction of diarization error rates. In particular, this research is focused on the segmentation and clustering stages within a diarization system. Although particular emphasis is placed on the broadcast news audio domain and systems developed throughout this work are also trained and tested on broadcast news data, the techniques proposed in this dissertation are also applicable to other domains including telephone conversations and meetings audio. Three main research themes were pursued: heuristic rules for speaker segmentation, modelling uncertainty in speaker model estimates, and modelling uncertainty in eigenvoice speaker modelling. The use of heuristic approaches for the speaker segmentation task was first investigated, with emphasis placed on minimizing missed boundary detections. A set of heuristic rules was proposed, to govern the detection and heuristic selection of candidate speaker segment boundaries. A second pass, using the same heuristic algorithm with a smaller window, was also proposed with the aim of improving detection of boundaries around short speaker segments. Compared to single threshold based methods, the proposed heuristic approach was shown to provide improved segmentation performance, leading to a reduction in the overall diarization error rate. Methods to model the uncertainty in speaker model estimates were developed, to address the difficulties associated with making segmentation and clustering decisions with limited data in the speaker segments. The Bayes factor, derived specifically for multivariate Gaussian speaker modelling, was introduced to account for the uncertainty of the speaker model estimates. The use of the Bayes factor also enabled the incorporation of prior information regarding the audio to aid segmentation and clustering decisions. The idea of modelling uncertainty in speaker model estimates was also extended to the eigenvoice speaker modelling framework for the speaker clustering task. Building on the application of Bayesian approaches to the speaker diarization problem, the proposed approach takes into account the uncertainty associated with the explicit estimation of the speaker factors. The proposed decision criteria, based on Bayesian theory, was shown to generally outperform their non- Bayesian counterparts.
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Yu, Yuan. "Tests of Independence in a Single 2x2 Contingency Table with Random Margins." Digital WPI, 2014. https://digitalcommons.wpi.edu/etd-theses/625.

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In analysis of the contingency tables, the Fisher's exact test is a very important statistical significant test that is commonly used to test independence between the two variables. However, the Fisher' s exact test is based upon the assumption of the fixed margins. That is, the Fisher's exact test uses information beyond the table so that it is conservative. To solve this problem, we allow the margins to be random. This means that instead of fitting the count data to the hypergeometric distribution as in the Fisher's exact test, we model the margins and one cell using multinomial distribution, and then we use the likelihood ratio to test the hypothesis of independence. Furthermore, using Bayesian inference, we consider the Bayes factor as another test statistic. In order to judge the test performance, we compare the power of the likelihood ratio test, the Bayes factor test and the Fisher's exact test. In addition, we use our methodology to analyse data gathered from the Worcester Heart Attack Study to assess gender difference in the therapeutic management of patients with acute myocardial infarction (AMI) by selected demographic and clinical characteristics.
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zheng, jiayin. "Calibrated Bayes Factor and Bayesian Model Averaging." The Ohio State University, 2018. http://rave.ohiolink.edu/etdc/view?acc_num=osu1518632917560265.

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Dávila, Calderón Ruben. "Creación de valor sobre la base del factor humano." Universidad Peruana de Ciencias Aplicadas (UPC), 2014. http://hdl.handle.net/10757/333085.

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El desarrollo y crecimiento de los negocios basa su consolidación en aspectos duros tanto como blandos, el tema es que los procedimientos duros como la tecnología cada vez son más símiles, adquiribles, mejorables, y de fácil implementación por parte de las organizaciones. El encontrar los “Tips” que permitan identificar aquellos elementos de la gestión de personas que agregan valor al negocio, sin duda se constituye en nuestros días en un factor determinante y estratégico en los negocios; por ende el presente documento presenta una aproximación de aquellos puntos de enlace que conectan el área de Recursos Humanos con las diferentes áreas “Core” y de soporte que dan base y estructura a la empresa.Por los resultados encontrados producto de la investigación realizada, podemos afirmar que lo que en algún tiempo fue una intención, luego una oportunidad de negocio, hoy se vuelve imperiosa y decisiva para considerar una empresa exitosa y con proyección.
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Trout, Alvin McKinley. "Further Study of the Gravity Loading Base Test Method." Thesis, Virginia Tech, 2000. http://hdl.handle.net/10919/35025.

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Presently, the industry accepted method for determining the positive moment strength of gravity loaded standing seam metal roof systems is the "Base Test Method". The Base Test Method provides a means for determining the positive moment strength of a multiple span, multiple purlin line standing seam roof system using the results from a set of six single span, simply supported, two-purlin line experimental tests. A set of six base tests must be conducted for each combination of purlin profile, deck panel profile, clip type, and intermediate bracing configuration. The primary objective of this study is to investigate the possibility of eliminating some of the roof system parameters specifically, clip type, purlin flange width, and roof panel thickness.

This study used the results from nine series of tests. Each series consists of 11 to 14 gravity loaded base tests. The first three series were used to examine the effects of clip type on the strength of standing seam roof system. The final six series was used to examine the effects of flange width and roof panel thickness. All nine series were constructed using Z-purlin sections with flanges facing the same direction (like orientation).

Based on the results of this study, clip type, purlin flange width, and roof panel thickness all have an effect on the strength of standing seam roof systems. Although none of the roof components can be completely eliminated from the required test matrix, by using trend relationships an acceptable test protocol was developed that results in a significant reduction in the number of required base tests.


Master of Science
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Amiss, Julie E. "Bayes factors : comparisons, simulation methods and influence." Thesis, University of Surrey, 1996. http://epubs.surrey.ac.uk/1014/.

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Shi, Xiaomeng Ph D. Massachusetts Institute of Technology. "Joint base-calling of two DNA sequences with factor graphs." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45868.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Electrical Engineering and Computer Science, 2008.
Includes bibliographical references (p. 63-65).
The advent of DNA sequencing has revolutionized biological research by providing virtual blueprints of living organisms and offering insights into complicated biochemical processes. DNA sequencing is a process encompassing both chemical reactions and signal processing techniques to identify the order of chemical bases in a DNA molecule. In this thesis, we focus on the base-calling stage, during which base order is estimated from data collected through electrophoresis and florescence detection. In particular, we examine the possibility of jointly base-calling two superposed DNA sequences by applying the sum-product algorithm on factor graphs. This approach allows a single electrophoresis experiment to process two sequences, using the same quantity of reagents and machine hours as for a single sequence. A practical heuristic is first used to estimate the peak parameters, then separate those into two sequences (major/minor) by passing messages on a factor graph. Base-calling on the major alone yields accuracy commensurate with single sequence approaches, and joint base-calling provides results for the minor which, while being of lesser quality, incurs no additional cost and can be ultimately used in the genome assembly process.
by Xiaomeng Shi.
S.M.
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Passiketopoulos, Prodromos Bolliet Louis. "Concepts de base et utilisation du réseau local industriel Factor." S.l. : Université Grenoble 1, 2008. http://dumas.ccsd.cnrs.fr/dumas-00322953.

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Tétin, Sébastien. "Microcapteurs chimiques à base de micropoutres en silicium modi?ées à l’aide de matériaux inorganiques microporeux." Thesis, Bordeaux 1, 2009. http://www.theses.fr/2009BOR13926/document.

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Afin d'optimiser l'utilisation des micropoutres en tant que capteurs chimiques, de nouvelles couches sensibles à base de matériaux microporeux ont été testées pour la détection d'humidité, de toluène et d'éthanol. Des essais sans couches sensibles ont aussi été effectués et des modèles simples ont été mis au point afin de prédire la réponse des micropoutres lors d'un changement d'environnement. Ces études ont donc permis la mise en oeuvre des micropoutres selon deux principes de détections différents: l'un reposant sur la variation de masse du capteur à base de micropoutre lors de l'absorption de composé par une couche sensible; l'autre reposant sur la détection de changements de propriétés physiques du fluide environnant
In order to optimize the use of microcantilever in the way of chemical sensing, microporous sensitive coatings have been tried to detect ethanol, toluene and humidity. The use of microcantilever without sensitive coating have been performed and simple models has been made and permit to predict the response of microcantilever in different environments. These studies rely on the use of microcantilever within two different detection mode: the detection of mass variation of the sensor because of the sorption of species in sensitive coating; and the detection of the change of physical properties of the fluid
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Books on the topic "Baye factor"

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Isabel, Lledó, ed. Equilibrio ácido-base: [un factor escencial para una salud óptima]. Barcelona: Hispano Europea, 2009.

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Guerrero, Alex. In balance for life: Understanding and maxmimizng your body's Ph factor. Gary City Park, NY: SquareOne Publishers, 2005.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. Bayes Factors for Forensic Decision Analyses with R. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0.

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Butterworth, Joel B. Soil erosion and causative factors at Vandenberg Air Force Base, California. [Kennedy Space Center, Florida]: National Aeronautics and Space Administration, John F. Kennedy Space Center, 1988.

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Elizabeth, Briggs, ed. York Factory medical journals, 1846-1849, at the Hudson's Bay Company archives. Winnipeg: Westgarth, 2000.

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Institute, American Petroleum. Risk-based inspection: Base resource document. Washington, D.C: American Petroleum Institute, 2000.

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1946-, Beardy Flora, and Coutts Robert 1953-, eds. Voices from Hudson Bay: Cree stories from York Factory. Montreal: McGill-Queen's University Press, 1996.

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Hayashi, Shun. Key Structural Factors of Group 5 Metal Oxide Clusters for Base Catalytic Application. Singapore: Springer Singapore, 2020. http://dx.doi.org/10.1007/978-981-15-7348-4.

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William, Barr, ed. From Barrow to Boothia: The Arctic journal of Chief Factor Peter Warren Dease, 1836-1839. Montreal: McGill-Queen's University Press, 2002.

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Brown, Ronald T., David O. Antonuccio, George J. DuPaul, Mary A. Fristad, Cheryl A. King, Laurel K. Leslie, Gabriele S. McCormick, William E. Pelham, John C. Piacentini, and Benedetto Vitiello. Childhood mental health disorders: Evidence base and contextual factors for psychosocial, psychopharmacological, and combined interventions. Washington: American Psychological Association, 2008. http://dx.doi.org/10.1037/11638-000.

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Book chapters on the topic "Baye factor"

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Kaliski, Burt. "Factor Base." In Encyclopedia of Cryptography and Security, 444. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-5906-5_406.

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Vexler, Albert, and Alan D. Hutson. "Bayes Factor." In Statistics in the Health Sciences, 129–64. Boca Raton, Florida : CRC Press, [2018]: Chapman and Hall/CRC, 2018. http://dx.doi.org/10.1201/b21899-5.

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Ślęzak, Dominik. "Rough Sets and Bayes Factor." In Transactions on Rough Sets III, 202–29. Berlin, Heidelberg: Springer Berlin Heidelberg, 2005. http://dx.doi.org/10.1007/11427834_10.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Model Choice." In Bayes Factors for Forensic Decision Analyses with R, 41–78. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_2.

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AbstractThis chapter addresses the problem of discrimination between competing propositions regarding selected features of a population of interest, also commonly known as “hypothesis testing”. Examples include counting processes when propositions refer to the proportion of items in a given population that show features of forensic interest (e.g., items with illegal content). Another typical example is the discrimination between competing propositions regarding the concentration of a controlled substance, such as drugs in blood, exceeding a given threshold. This chapter develops and explains the use of the Bayes factor for one-sided hypothesis testing involving model parameters in the form of a proportion and a mean. In both situations, additional factors (e.g., errors) are considered as well as aspects of decision making.
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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Investigative Purposes." In Bayes Factors for Forensic Decision Analyses with R, 141–76. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_4.

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AbstractThis chapter develops and discusses Bayes factors for investigative purposes, i.e. situations in which no potential source is available for comparison purposes. A typical example for this is the problem of classifying items or individuals into one of several classes or populations on the basis of available data (e.g., measurements of one or more attributes). More specifically, material of interest is analyzed (e.g., the quantity of cocaine present on banknotes) and results are evaluated in terms of their effect on the odds in favor of a proposition according to which the recovered material originates from a given population (e.g., banknotes in general circulation), compared to an alternative proposition according to which the recovered items originate from another population (e.g., banknotes related to drug trafficking). The problem of discrimination between populations is addressed for various types of discrete and continuous data, respectively, including an extension to continuous multivariate data. The examples developed in this chapter involve classification for two or more populations. The assessment of model performance is addressed as well.
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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Bayes Factor for Evaluative Purposes." In Bayes Factors for Forensic Decision Analyses with R, 79–139. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_3.

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AbstractThis chapter presents and discusses the use of the Bayes factor for the evaluation of scientific evidence in the form of discrete, continuous and continuous multivariate data. The latter may present a complex dependence structure that will be handled by means of multilevel models. The notion of “evaluative purpose” is understood here as referring to situations in which material of known source (control material) and evidential material of unknown source (recovered or questioned material) is collected and analyzed. The purpose is to evaluate the effect of the output of the examinations, in the form of scores or measurements of features, on the odds in favor of a proposition put forward by the prosecution, compared to an alternative proposition advanced by the defence. A discussion is included of the sensitivity of the described Bayes factor procedures to changes in the features of recovered and control materials, the available background information, as well as to choices made during probabilistic modelling and prior elicitation.
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De Santis, Fulvio, and Fulvio Spezzaferri. "Comparing hierarchical models using Bayes factor and fractional Bayes factor: a robust analysis." In Institute of Mathematical Statistics Lecture Notes - Monograph Series, 305–14. Hayward, CA: Institute of Mathematical Statistics, 1996. http://dx.doi.org/10.1214/lnms/1215453075.

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Link, William A., and Richard J. Barker. "Bayes Factors and Multimodel Inference." In Modeling Demographic Processes In Marked Populations, 595–615. Boston, MA: Springer US, 2009. http://dx.doi.org/10.1007/978-0-387-78151-8_26.

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Bozza, Silvia, Franco Taroni, and Alex Biedermann. "Introduction to the Bayes Factor and Decision Analysis." In Bayes Factors for Forensic Decision Analyses with R, 1–40. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-09839-0_1.

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AbstractThis chapter presents an overview of statistics in forensic science, with an emphasis on the Bayesian perspective and the role of the Bayes factor in logical inference and decision. The chapter introduces the reader to three key topics that forensic scientists commonly encounter and that are treated in this book: model choice, evaluation and investigation. For each of these themes, Bayes factors will be developed in later chapters and discussed using practical examples. Particular attention will be given to the distinction between feature- and score-based Bayes factors, representing different approaches to deal with input information (i.e., measurements). This introductory chapter also provides theoretical background that analysts might need during data analysis, including elements of forensic interpretation, computational methods, decision theory, prior elicitation and sensitivity analysis.
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Kafri, Uri, and Yoseph Yechieli. "Factors Controlling Base-Level Elevation Changes." In Groundwater Base Level Changes and Adjoining Hydrological Systems, 7–19. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-13944-4_3.

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Conference papers on the topic "Baye factor"

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Giffin, A., J. D. Skufca, and P. A. Lao. "Using Bayes factors for multi-factor, biometric authentication." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING (MAXENT 2014). AIP Publishing LLC, 2015. http://dx.doi.org/10.1063/1.4906029.

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Anuzis, Andrius, and Kevin O. Lillehei. "Predictive Factors for Surgical Intervention in Prolactinomas." In 31st Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2022. http://dx.doi.org/10.1055/s-0042-1743763.

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Kim, Minyoung, Yuting Wang, Pritish Sahu, and Vladimir Pavlovic. "Bayes-Factor-VAE: Hierarchical Bayesian Deep Auto-Encoder Models for Factor Disentanglement." In 2019 IEEE/CVF International Conference on Computer Vision (ICCV). IEEE, 2019. http://dx.doi.org/10.1109/iccv.2019.00307.

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Running, During. "Biomechanical Model of Bare-Breasts." In Applied Human Factors and Ergonomics Conference. AHFE International, 2020. http://dx.doi.org/10.54941/ahfe100423.

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Sports bras are designed to reduce mammary glands or breast movement during exercises, but there is no standardized, valid and reliable method to evaluate relative three-dimensional (3D) breast movement; and there is no literature to predict the 3D force acting on the breasts during activities. A reliable method is essential to evaluate 3D breast movement and to determine the effective design features of supportive sports bras. This study derived and validated a new Breast Coordinate System (BCS) for investigating 3D breast movement, so as to identify the most effective bra features and to analyze the effects of breast volume and bra strap properties on breast movement, then to develop theoretical models of breast force generated during bare-breasted running. In the light of this, 3D mechanical models have been developed based on a system comprising a mass, springs and dampers. The orthogonal force exerted on the breasts during running was derived. The predicted results of maximum breast force were verified with previous literature. The new methods will contribute to future research on human locomotion and the design of close-fitting garments.
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Riadi, Doni, and Ade Geovania Azwar. "BASE SINGLE PART PRODUCT QUALITY CONTROL USING STATISTICAL QUALITY CONTROL (SQC) METHOD IN PT DIRGANTARA INDONESIA MACHINING DEPARTMENT." In Seminar Sosial Politik, Bisnis, Akuntansi dan Teknik (SoBAT) ke-3. LPPM USB YPKP, 2021. http://dx.doi.org/10.32897/sobat3.2021.45.

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Base Single Part is the main component of the cable installation that functions as a connector between cables in the aircraft. As a critical part, the Base Single Part is the most important sub-component that must be completed to be installed in the aircraft part. Base Single Part is a part that is used in all types of aircraft which are specially produced at PT. Dirgantara Indonesia, in one aircraft this component can be installed in the amount of 8000-9000 Base Single Part. In relation to the request from the Final Assembly Line section regarding the request for the Base Single Part component, there are still some products that do not meet the specifications so that they have to be repaired, such as holes that do not match the size, this causes the delivery order process to the Final Assembly Line section to be hampered and disruptive smooth production. The purpose of this research is to control the quality of the Base Single Part product by using a p control chart, and a causal diagram.The results of the study conclude that the calculation of the percentage level of defect in the Base Single Part component using the p control chart, there are 3 percentage points that are outside the control limit, namely in August with a disability percentage of 15.11%, in September 10.32%, and October 15.60%. Factors causing defects in Base Single Part components consist of 3 factors, human factors, method factors and machine factors. The human factor is workers who are less productive. Furthermore, the method factor is regarding the fluctuating production schedule and the last cause is the machine factor that lacks maintenance.
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Lehmberg, Jens, A. Fritsche, B. Schatlo, S. Duran, V. Rhode, C. Freyschlag, C. Thome, et al. "Duration of Trigeminal Neuralgia Is the Risk Factor for Failure of Microvascular Decompression." In 29th Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-1679546.

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Wang, D., R. Vogt, and S. Sridharan. "Bayes Factor based speaker clustering for speaker diarization." In 2010 10th International Conference on Information Sciences, Signal Processing and their Applications (ISSPA). IEEE, 2010. http://dx.doi.org/10.1109/isspa.2010.5605553.

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Wang, D., Robert Vogt, and Sridha Sridharan. "Bayes factor based speaker segmentation for speaker diarization." In Interspeech 2010. ISCA: ISCA, 2010. http://dx.doi.org/10.21437/interspeech.2010-427.

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Fan, Zhenfeng. "Crest factor reduction for TD-LTE base station." In 2016 IEEE Information Technology, Networking, Electronic and Automation Control Conference (ITNEC). IEEE, 2016. http://dx.doi.org/10.1109/itnec.2016.7560514.

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Berman, Ethan, Chandala Chitguppi, Mindy Rabinowitz, Gurston Nyquist, Judd Fastenberg, Tomas Garzon, Ethan Moritz, Christopher Farrell, Marc Rosen, and James Evans. "Recurrent Pituitary Adenoma: Analysis of Risk Factors and Surgical Morbidity." In 29th Annual Meeting North American Skull Base Society. Georg Thieme Verlag KG, 2019. http://dx.doi.org/10.1055/s-0039-1679530.

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Reports on the topic "Baye factor"

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Cegan, Jeffrey, George Calfas, and Matthew Bates. Identifying key factors relevant for base camp siting. Engineer Research and Development Center (U.S.), March 2018. http://dx.doi.org/10.21079/11681/26472.

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Hilbrecht, Margo, David Baxter, Alexander V. Graham, and Maha Sohail. Research Expertise and the Framework of Harms: Social Network Analysis, Phase One. GREO, December 2020. http://dx.doi.org/10.33684/2020.006.

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In 2019, the Gambling Commission announced a National Strategy to Reduce Gambling Harms. Underlying the strategy is the Framework of Harms, outlined in Measuring gambling-related harms: A framework for action. "The Framework" adopts a public health approach to address gambling-related harm in Great Britain across multiple levels of measurement. It comprises three primary factors and nine related subfactors. To advance the National Strategy, all componentsneed to be supported by a strong evidence base. This report examines existing research expertise relevant to the Framework amongacademics based in the UK. The aim is to understand the extent to which the Framework factors and subfactors have been studied in order to identify gaps in expertise and provide evidence for decision making thatisrelevant to gambling harms research priorities. A social network analysis identified coauthor networks and alignment of research output with the Framework. The search strategy was limited to peer-reviewed items and covered the 12-year period from 2008 to 2019. Articles were selected using a Web of Science search. Of the 1417 records identified in the search, the dataset was refined to include only those articles that could be assigned to at least one Framework factor (n = 279). The primary factors and subfactors are: Resources:Work and Employment, Money and Debt, Crime;Relationships:Partners, Families and Friends, Community; and Health:Physical Health, Psychological Distress, and Mental Health. We used Gephi software to create visualisations reflecting degree centrality (number of coauthor networks) so that each factor and subfactor could be assessed for the density of research expertise and patterns of collaboration among coauthors. The findings show considerable variation by framework factor in the number of authors and collaborations, suggesting a need to develop additional research capacity to address under-researched areas. The Health factor subcategory of Mental Health comprised almost three-quarters of all citations, with the Resources factor subcategory of Money and Debt a distant second at 12% of all articles. The Relationships factor, comprised of two subfactors, accounted for less than 10%of total articles. Network density varied too. Although there were few collaborative networks in subfactors such as Community or Work and Employment, all Health subfactors showed strong levels of collaboration. Further, some subfactors with a limited number of researchers such as Partners, Families, and Friends and Money and debt had several active collaborations. Some researchers’ had publications that spanned multiple Framework factors. These multiple-factor researchers usually had a wide range of coauthors when compared to those who specialised (with the exception of Mental Health).Others’ collaborations spanned subfactors within a factor area. This was especially notable forHealth. The visualisations suggest that gambling harms research expertise in the UK has considerable room to grow in order to supporta more comprehensive, locally contextualised evidence base for the Framework. To do so, priority harms and funding opportunities will need further consideration. This will require multi-sector and multidisciplinary collaboration consistent with the public health approach underlying the Framework. Future research related to the present analysis will explore the geographic distribution of research activity within the UK, and research collaborations with harms experts internationally.
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Adlakha, Deepi, Jane Clarke, Perla Mansour, and Mark Tully. Walk-along and cycle-along: Assessing the benefits of the Connswater Community Greenway in Belfast, UK. Property Research Trust, 2021. http://dx.doi.org/10.52915/ghcj1777.

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Physical inactivity is a risk factor for numerous chronic diseases, and a mounting global health problem. It is likely that the outdoor physical environment, together with social environmental factors, has a tendency to either promote or discourage physical activity, not least in cities and other urban areas. However, the evidence base on this is sparse, making it hard to identify the best policy interventions to make, at the local or city level. This study seeks to assess the impact of one such intervention, the Connswater Community Greenway CCG), in Belfast, in Northern Ireland, UK. To do that it uses innovative methodologies, ‘Walk-along’ and ‘Cycle-along’ that involve wearable sensors and video footages, to improve our understanding of the impact of the CCG on local residents. The findings suggest that four characteristics of the CCG affect people’s activity and the benefits that the CCG created. These are physical factors, social factors, policy factors and individual factors. Each of these has many elements, with different impacts on different people using the greenway.
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Walsh, Daniel C., and Adrian E. Raftery. Classification of Mixtures of Spatial Point Processes via Partial Bayes Factors. Fort Belvoir, VA: Defense Technical Information Center, January 2003. http://dx.doi.org/10.21236/ada459757.

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Emond, Mary J., Adrian E. Raftery, and Russell J. Steele. Easy Computation of Bayes Factors and Normalizing Constants for Mixture Models via Mixture Importance Sampling. Fort Belvoir, VA: Defense Technical Information Center, July 2001. http://dx.doi.org/10.21236/ada459760.

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Caton, Howell. Editing a Distributed Fact Base With an ORG Chart. Fort Belvoir, VA: Defense Technical Information Center, September 2000. http://dx.doi.org/10.21236/ada393474.

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Czado, Claudia, and Adrian E. Raftery. Choosing the Link Function and Accounting for Link Uncertainty in Generalized Linear Models using Bayes Factors. Fort Belvoir, VA: Defense Technical Information Center, October 2001. http://dx.doi.org/10.21236/ada459482.

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Satagopan, Jaya M., Michael A. Newton, and Adrian E. Raftery. Easy Estimation of Normalizing Constants and Bayes Factors from Posterior Simulation: Stabilizing the Harmonic Mean Estimator. Fort Belvoir, VA: Defense Technical Information Center, November 2000. http://dx.doi.org/10.21236/ada459836.

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Roesler, Jeffery, Roberto Montemayor, John DeSantis, and Prakhar Gupta. Evaluation of Premature Cracking in Urban Concrete Pavement. Illinois Center for Transportation, January 2021. http://dx.doi.org/10.36501/0197-9191/21-001.

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This study investigated the causes for premature, transverse cracking on urban jointed plain concrete pavements in Illinois. A field survey of 67 sections throughout Illinois coupled with ultrasonic evaluation was completed to synthesize the extent of premature cracking on urban JPCP. The visual survey showed some transverse and longitudinal cracks were a result of improper slab geometry (excessive slab length and width). Ultrasonic tests over the contraction joints determined some notched joints had not activated and adjacent transverse cracks were likely formed as a result. Three-dimensional finite-element analyses confirmed that cracking would not develop as a result of normal environmental factors and slab-base frictional restraint. The concrete mixture also did not appear to be a contributing factor to the premature cracks. Finally, the lack of lubrication on dowel bars was determined to potentially be a primary mechanism that could restrain the transverse contraction joints, produce excessive tensile stresses in the slab, and cause premature transverse cracks to develop.
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Moore, David W., and Thomas M. Dillon. Chronic Sublethal Effects of San Francisco Bay Sediments on Nereis (Neanthes) Arenaceodentata: Non treatment Factors. Fort Belvoir, VA: Defense Technical Information Center, September 1992. http://dx.doi.org/10.21236/ada259400.

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