Relevant bibliographies by topics / Beta Distribution

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  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Beta Distribution'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 September 2023

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Journal articles on the topic "Beta Distribution"

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Wood, A., and K. J. Beven. "On runoff generation and the distribution of storage deficits." Hydrology Research 44, no. 4 (March 14, 2013): 673–89. http://dx.doi.org/10.2166/nh.2013.119.

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A number of hydrological models use a distribution function to develop the non-linear rainfall–runoff catchment response. In this study the beta function is applied to represent a distribution of soil moisture storages in conjunction with a fast and slow pathway routing. The BETA3 and BETA4 modules, presented in this paper, have a distribution of discrete storage elements that have variable and redistributed water levels at each timestep. The PDM-BETA5 is an analytical solution with a similar structure to the commonly used probability distribution model (PDM). Model testing was performed on three catchments in the Northern Pennine region in England. The performances of the BETA models were compared with a commonly used formulation of the PDM. The BETA models performed marginally better than the PDM in calibration and parameter estimation was better with the BETA models than for the PDM. The BETA models had a small advantage in validation on the hydrologically fast responding test catchments.
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Gupta, Arjun K., and Daya K. Nagar. "Matrix-variate beta distribution." International Journal of Mathematics and Mathematical Sciences 24, no. 7 (2000): 449–59. http://dx.doi.org/10.1155/s0161171200002398.

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We propose matrix-variate beta type III distribution. Several properties of this distribution including Laplace transform, marginal distribution and its relationship with matrix-variate beta type I and type II distributions are also studied.
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Gupta, Arjun K., and Saralees Nadarajah. "Beta Bessel distributions." International Journal of Mathematics and Mathematical Sciences 2006 (2006): 1–14. http://dx.doi.org/10.1155/ijmms/2006/16156.

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Three new distributions on the unit interval[0,1]are introduced which generalize the standard beta distribution. These distributions involve the Bessel function. Expression is derived for their shapes, particular cases, and thenth moments. Estimation by the method of maximum likelihood and Bayes estimation are discussed. Finally, an application to consumer price indices is illustrated to show that the proposed distributions are better models to economic data than one based on the standard beta distribution.
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Nagar, Daya K., and Arjun K. Gupta. "Matrix-variate Kummer-Beta distribution." Journal of the Australian Mathematical Society 73, no. 1 (August 2002): 11–26. http://dx.doi.org/10.1017/s1446788700008442.

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AbstractThis paper proposes matrix variate generalization of Kummer-Beta family of distributions which has been studied recently by Ng and Kotz. This distribution is an extension of Beta distribution. Its characteristic function has been derived and it is shown that the distribution is orthogonally invariant. Some results on distribution of random quadratic forms have also been derived.
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Awodutire, Phillip Oluwatobi, Oluwafemi Samson Balogun, Akintayo Kehinde Olapade, and Ethelbert Chinaka Nduka. "The modified beta transmuted family of distributions with applications using the exponential distribution." PLOS ONE 16, no. 11 (November 18, 2021): e0258512. http://dx.doi.org/10.1371/journal.pone.0258512.

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In this work, a new family of distributions, which extends the Beta transmuted family, was obtained, called the Modified Beta Transmuted Family of distribution. This derived family has the Beta Family of Distribution and the Transmuted family of distribution as subfamilies. The Modified beta transmuted frechet, modified beta transmuted exponential, modified beta transmuted gompertz and modified beta transmuted lindley were obtained as special cases. The analytical expressions were studied for some statistical properties of the derived family of distribution which includes the moments, moments generating function and order statistics. The estimates of the parameters of the family were obtained using the maximum likelihood estimation method. Using the exponential distribution as a baseline for the family distribution, the resulting distribution (modified beta transmuted exponential distribution) was studied and its properties. The modified beta transmuted exponential distribution was applied to a real life time data to assess its flexibility in which the results shows a better fit when compared to some competitive models.
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Díaz-García, José A. "Riesz and beta-Riesz distributions." Austrian Journal of Statistics 45, no. 2 (February 29, 2016): 35–51. http://dx.doi.org/10.17713/ajs.v45i2.55.

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This article derives several properties of the Riesz distributions, such as their corresponding Bartlett decompositions, the inverse Riesz distributions and the distribution of the generalised variance for real normed division algebras. In addition, introduce a kind of generalised beta distribution termed beta-Riesz distribution for real normed division algebras. Two versions of this distributions are proposed and some properties are studied.
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Makubate, Boikanyo, Broderick O. Oluyede, Gofaone Motobetso, Shujiao Huang, and Adeniyi F. Fagbamigbe. "The Beta Weibull-G Family of Distributions: Model, Properties and Application." International Journal of Statistics and Probability 7, no. 2 (January 18, 2018): 12. http://dx.doi.org/10.5539/ijsp.v7n2p12.

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A new family of generalized distributions called the beta Weibull-G (BWG) distribution is proposed and developed. This new class of distributions has several new and well known distributions including exponentiated-G, Weibull-G, Rayleigh-G, exponential-G, beta exponential-G, beta Rayleigh-G, beta Rayleigh exponential, beta-exponential-exponential, Weibull-log-logistic distributions, as well as several other distributions such as beta Weibull-Uniform, beta Rayleigh-Uniform, beta exponential-Uniform, beta Weibull-log logistic and beta Weibull-exponential distributions as special cases. Series expansion of the density function, hazard function, moments, mean deviations, Lorenz and Bonferroni curves, R\'enyi entropy, distribution of order statistics and maximum likelihood estimates of the model parameters are given. Application of the model to real data set is presented to illustrate the importance and usefulness of the special case beta Weibull-log-logistic distribution.
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Okubo, Tomoya, and Shin-ichi Mayekawa. "Approximating score distributions using mixed-multivariate beta distribution." Behaviormetrika 44, no. 2 (March 20, 2017): 369–84. http://dx.doi.org/10.1007/s41237-017-0019-7.

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Polosin, V. G. "Shape measures for the generalized beta exponential distribution." Journal of Physics: Conference Series 2094, no. 2 (November 1, 2021): 022022. http://dx.doi.org/10.1088/1742-6596/2094/2/022022.

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Abstract This paper contains parametric and informational shape measures for various families of the generalized beta exponential distribution since it is important to determination of the distribution shape for analysing an experimental data set. A logistic parameter is used to select independent types of beta exponential distributions, that it allows to combine the distributions of different subfamilies. In this paper the use of parametric shape measures to pre-define distribution shape is discusses. In particular, the initial and standard central moments for the main types of generalized beta exponential distribution are given. In the paper it is proposes to use the entropy coefficient of unshifted distribution as an independent information measure of the shape of unshifted generalized beta exponential distributions. In order to increase the reliability of the preliminary determination of the shape of the model, expressions for the entropy coefficient of shifted families both the generalized beta exponential distributions of the first and second types, and the generalized gamma exponential distribution were obtained. For practical applied the entropy coefficients of unshifted distributions for various subfamilies of generalized beta exponential distributions can be useful.
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Adeleke, Maradesa. "Beta-Hyperhalfnormal Distribution and Its Application." BASRA JOURNAL OF SCIENCE 38, no. 2 (April 1, 2020): 131–56. http://dx.doi.org/10.29072/basjs.202021.

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This research developed Hyper halfnormal distribution (HHND) and Beta-hyper halfnormal distribution (BHHND). The statistical properties of those distributions were studied and BHHND is found to have bathtube hazard function. The distributions are fitted to lifetime data that seemed to have bathtube distribution. From the analysis, the definition of HHND depends on the value of p and q (mixing proportion) and coefficient of variation. For q > p, we have Hyper halfnormal and it is Hypo-halfnormal if otherwise. The BHHND is useful in modeling heavily skewed, non-normal data with bathtube hazard function.
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Dissertations / Theses on the topic "Beta Distribution"

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Owen, Claire Elayne Bangerter. "Parameter Estimation for the Beta Distribution." Diss., CLICK HERE for online access, 2008. http://contentdm.lib.byu.edu/ETD/image/etd2670.pdf.

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Urashkin, Alexander. "Beta dose distribution for randomly packed microspheres." Texas A&M University, 2006. http://hdl.handle.net/1969.1/4854.

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Brachytherapy refers to the therapeutic use of encapsulated radionuclides within or close to a tumor. Today brachytherapy is used as an alternative to surgery or beam therapy to treat different kinds of cancers such as breast, lung, prostate, ovarian and pancreatic, primary and metastatic hepatic tumors and rheumatoid arthritis. Microspheres are one therapy utilized in brachytherapy procedures. Despite the development of advanced equipment and methods, there is still a limited knowledge of radiation dose distribution when utilizing this technique. This study focuses on random packing of microspheres and seeks to determine dose distributions for specific cases. The Monte Carlo Neutral Particle code (MCNP) developed by Los Alamos National Laboratory is used to simulate beta particle transport. Pr-142 is the beta source utilized for all calculations. The cylinder radii are 0.1, 0.15 and 0.3 cm and sphere radii are 0.03, 0.05 and 0.07 cm. The results are verified by examining limiting cases: uniformly distributed source and line of microspheres. Based on the data collected, the correlations between the average dose, its related variance, and distance from the cylinder were determined. An approach for estimating the surface average dose was developed and suggestions regarding an approach to assess surface variance estimation were presented.
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Jonsson, Fredrik. "Statistical studies of the Beta Gumbel distribution." Thesis, Uppsala universitet, Tillämpad matematik och statistik, 2014. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220547.

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Feng, Jingyu. "Modeling Distributions of Test Scores with Mixtures of Beta Distributions." Diss., CLICK HERE for online access, 2005. http://contentdm.lib.byu.edu/ETD/image/etd1068.pdf.

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Paz, Rosineide Fernando da. "Alternative regression models to beta distribution under bayesian approach." Universidade Federal de São Carlos, 2017. https://repositorio.ufscar.br/handle/ufscar/9146.

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Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)
The Beta distribution is a bounded domain distribution which has dominated the modeling the distribution of random variable that assume value between 0 and 1. Bounded domain distributions arising in various situations such as rates, proportions and index. Motivated by an analysis of electoral votes percentages (where a distribution with support on the positive real numbers was used, although a distribution with limited support could be more suitable) we focus on alternative distributions to Beta distribution with emphasis in regression models. In this work, initially we present the Simplex mixture model as a flexible model to modeling the distribution of bounded random variable then we extend the model to the context of regression models with the inclusion of covariates. The parameters estimation is discussed for both models considering Bayesian inference. We apply these models to simulated data sets in order to investigate the performance of the estimators. The results obtained were satisfactory for all the cases investigated. Finally, we introduce a parameterization of the L-Logistic distribution to be used in the context of regression models and we extend it to a mixture of mixed models.
A distribuição beta é uma distribuição com suporte limitado que tem dominado a modelagem de variáveis aleatórias que assumem valores entre 0 e 1. Distribuições com suporte limitado surgem em várias situações como em taxas, proporções e índices. Motivados por uma análise de porcentagens de votos eleitorais, em que foi assumida uma distribuição com suporte nos números reais positivos quando uma distribuição com suporte limitado seira mais apropriada, focamos em modelos alternativos a distribuição beta com enfase em modelos de regressão. Neste trabalho, apresentamos, inicialmente, um modelo de mistura de distribuições Simplex como um modelo flexível para modelar a distribuição de variáveis aleatórias que assumem valores em um intervalo limitado, em seguida estendemos o modelo para o contexto de modelos de regressão com a inclusão de covariáveis. A estimação dos parâmetros foi discutida para ambos os modelos, considerando o método bayesiano. Aplicamos os dois modelos a dados simulados para investigarmos a performance dos estimadores usados. Os resultados obtidos foram satisfatórios para todos os casos investigados. Finalmente, introduzimos a distribuição L-Logistica no contexto de modelos de regressão e posteriormente estendemos este modelo para o contexto de misturas de modelos de regressão mista.
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Yan, Huey. "A Comparison of Estimation Procedures for the Beta Distribution." DigitalCommons@USU, 1991. https://digitalcommons.usu.edu/etd/7126.

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The beta distribution may be used as a stochastic model for continuous proportions in many situations in applied statistics. This thesis was concerned with estimation of the parameters of the beta distribution in three different situations. Three different estimation procedures-the method of moments, maximum likelihood, and a hybrid of these two methods, which we call the one-step improvement-were compared by computer simulation, for beta data and beta data contaminated by zeros and ones. We also evaluated maximum likelihood estimation in the context of censored data, and Newton's method as a numerical procedure for solving the likelihood equations for censored beta data.
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Ribom, Henrik, and Mathias Sjöberg. "Intraday Analysis & Prediction of Volume Distribution on the Stockholm Stock Exchange : An exploratory study of volume distribution and automated trading." Thesis, KTH, Matematisk statistik, 2015. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-170148.

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The purpose of this study is to create a model of prediction for the volume distribution. Due to the lack of previous studies on the subject, an exploratory approach is used, with the purpose of serving as a proof of concept for further research. By looking at all market data from the Stockholm stock exchange the volume distribution of individual order books are matched with a mixed beta distribution and scaled by a prediction based on a linear regression. The model provided in this study outperforms the floating mean by quite a good margin. Some days are, almost by definition, impossible to get an accurate prediction on. Intraday news with a big impact have a tendency to skew the results away from the predicted value. To remedy this the initial model is expanded by using intraday data to catch up on trends
Syftet med denna rapport är att skapa en model för prediction av höglikvida aktiers volym fördelingen på stockholmsbörsen. Detta görs på ett utforskande sätt och agerar som konceptvalidering och bevis att grunda vidare forsking på. Genom att titta på all marknadsdata på stockholmsbörsen kommer den kumulativa volym fördelingen av induviduela aktier skapas. För att sedan bli matchad mot en mixture beta fördeling och skalas med en prediktion erhållen från en linjär regrission. Modelen som presenteras i rapporten fungerar bättre som prediktion än det flytande medelet. Det finns dock dagar som av sin natur är omöjliga att förutspå, exempelvis när en stor nyhet blir känd. För att kompensera för detta expanderas modelen genom att använda data från samma dag som ska prediceras och detta förbättrar modelen för den resterande tiden av dagen.
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SANTOS, Rosilda Sousa. "Estudo sobre algumas famílias de distribuições de probabilidades generalizadas." Universidade Federal de Campina Grande, 2012. http://dspace.sti.ufcg.edu.br:8080/jspui/handle/riufcg/1358.

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Capes
A proposta desta dissertação está relacionada com o estudo das principais famílias de distribuições de probabilidade generalizadas. Particularmente, estudamos as distribuições Beta Pareto, Beta Exponencial Generalizada, Beta Weibull Modificada, Beta Fréchet e a Kw-G. Para cada uma delas foram obtidas expressões para as funções densidades de probabilidade, funcões de distribuição acumuladas, funções de taxa de falha, funções geratrizes de momentos, bem como foram obtidos os estimadores dos parâmetros pelo método da máxima verossimilhança. Finalmente, para cada distribuição foram feitas aplicações com dados reais.
The purpose of this dissertation is to study the main families of generalized probability distributions. Particularly we study the distributions Beta Pareto, generalized Beta Exponential, Beta Modified Weibull, Beta Fréchet and Kw-G. For each one of these distributions we obtain expressions for the probability density function, cumulative distribution function, hazard function and moment generating function as well as parameter estimates by the method of maximum likelihood. Finally, we make real data applications for each one of the studied distributions.
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Isik, Mehmet. "Thermally Stimulated Current Study Of Traps Distribution In Beta-tlins2 Layered Crystals." Master's thesis, METU, 2008. http://etd.lib.metu.edu.tr/upload/12609667/index.pdf.

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Trapping centres in as-grown TlInS2 layered single crystals have been studied by using a thermally stimulated current (TSC) technique. TSC measurements have been performed in the temperature range of 10-300 K with various heating rates. Experimental evidence has been found for the presence of five trapping centres with activation energies 12, 14, 400, 570 and 650 meV. Their capture cross-sections and concentrations were also determined. It is concluded that in these centres retrapping is negligible as confirmed by the good agreement between the experimental results and the theoretical predictions of the model that assumes slow retrapping. An exponential distribution of traps was revealed from the analysis of the TSC data obtained at different light excitation temperatures. The transmission and reflection spectra of TlInS2 crystals were measured over the spectral region of 400-1100 nm to determine the absorption coefficient and refractive index. The analysis of the room temperature absorption data revealed the coexistence of the indirect and direct transitions. The absorption edge was observed to shift toward the lower energy values as temperature increases from 10 to 300 K. The oscillator and the dispersion energies, and the zero-frequency refractive index were also reported. Furthermore, the chemical composition of TlInS2 crystals was determined from energy dispersive spectroscopic analysis. The parameters of monoclinic unit cell were found by studying the x-ray powder diffraction.
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OLIVEIRA, Cícero Carlos Felix de. "Uma priori beta para distribuição binomial negativa." Universidade Federal Rural de Pernambuco, 2011. http://www.tede2.ufrpe.br:8080/tede2/handle/tede2/4537.

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This dissertation is being dealt with a discrete distribution based on Bernoulli trials, which is the Negative Binomial distribution. The main objective is to propose a new non-informative prior distribution for the Negative Binomial model, which is being termed as a possible prior distribution Beta(0; 0), which is an improper distribution. This distribution is also known for the Binomial model as Haldane prior, but for the Negative Binomial model there are no studies to date. The study of the behavior of this prior was based on Bayesian and classical contexts. The idea of using a non-informative prior is the desire to make statistical inference based on the minimum of information prior subjective as possible. Well, makes it possible to compare the results of classical inference that uses only sample information, for example, the maximum likelihood estimator. When is compared the Beta(0; 0) distribution with the Bayes-Laplace prior and Jeffreys prior, based on the Bayesian estimators (posterior mean and posterior mode) and the maximum likelihood estimator, note that the possible Beta(0; 0) prior is less informative than the others prior. It is also verified that is prior possible is a limited distribution in parameter space, thus, an important feature for non-informative prior. The main argument shows that the possible Beta(0; 0) prior is adequate, when it is applied in a predictive posterior distribution for Negative Binomial model, leading the a Beta-Negative Binomial distribution (which corresponds the a hypergeometric multiplied by a probability). All observations citas are strengthened by several studies, such as: basic concepts related to Bayesian Inference and concepts of the negative binomial distribution and Beta-Negative Binomial (a mixture of Beta with the negative binomial) distribution.
Nesta dissertação está sendo abordado uma distribuição discreta baseada em ensaios de Bernoulli, que é a distribuição Binomial Negativa. O objetivo principal é prôpor uma nova distribuição a priori não informativa para o modelo Binomial Negativa, que está sendo denominado como uma possível distribuição a priori Beta(0; 0), que é uma distribuição imprópria. Essa distribuição também é conhecida para o modelo Binomial como a priori de Haldane, mas para o modelo Binomial Negativa não há nenhum estudo até o momento. O estudo do comportamento desta a priori foi baseada nos contextos bayesiano e clássico. A ideia da utilização de uma a priori não informativa é o desejo de fazer inferência estatística baseada no mínimo de informação subjetiva a priori quanto seja possível. Assim, torna possível a comparação com os resultados da inferência clássica que só usa informação amostral, como por exemplo, o estimador de máxima verossimilhança. Quando é comparado a distribuição Beta(0; 0) com a priori de Bayes - Laplace e a priori de Jeffreys, baseado-se nos estimadores bayesiano (média a posteriori e moda a posteriori) e no estimador de máxima verossimilhança, nota-se que a possível a priori Beta(0; 0) é menos informativa do que as outras a priori. É verificado também, que esta possível a priori é uma distribuição limitada no espaço paramétrico, sendo assim, uma característica importante para a priori não informativa. O principal argumento mostra que a possível a priori Beta(0; 0) é adequada, quando ela é aplicada numa distribuição a posteriori preditiva para modelo Binomial Negativa, levando a uma distribuição Beta Binomial Negativa (que corresponde a uma hipergeométrica multiplicada por uma probabilidade). Todas as observações citadas são fortalecidas por alguns estudos feitos, tais como: conceitos básicos associados à Inferência Bayesiana e conceitos das distribuições Binomial Negativa e Beta Binomial Negativa (que uma mistura da Beta com a Binomial Negativa).
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Books on the topic "Beta Distribution"

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Letschert, J. P. W. Beta section Beta: Biogeographical patterns of variation, and taxonomy. Wageningen, the Netherlands: Wageningen Agricultural University, 1993.

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Boyle, G. E. A simple measure of B(beta)-convergence. Maynooth, Co Kildare: Maynooth College, Department of Economics, 1995.

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Bolton, David W. Occurrence and distribution of radium, gross alpha-particle activity, and gross beta-particle activity in ground water in the Magothy Formation and Potomac Group aquifers, Upper Chesapeake Bay area, Maryland. [Baltimore, Md.]: Dept. of Natural Resources, Resource Assessment Service, Maryland Geological Survey, 2000.

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Kotz, Samuel. Beyond beta: Other continuous families of distributions with bounded support and applications. Singapore: World Scientific, 2005.

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Letchert, J. P. W. Beta Section Beta (Wageningen Agricultural University Papers). Backhuys Publishers, 1994.

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Nadarajah, Saralees, and Arjun K. Gupta. Handbook of Beta Distribution and Its Applications. Taylor & Francis Group, 2004.

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K, Gupta A., and Saralees Nadarajah. Handbook of Beta Distribution and Its Applications. Taylor & Francis Group, 2020.

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Nadarajah, Saralees, and Arjun K. Gupta. Handbook of Beta Distribution and Its Applications. Taylor & Francis Group, 2004.

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1938-, Gupta A. K., and Nadarajah Saralees, eds. Handbook of beta distribution and its applications. New York: Marcel Dekker, 2004.

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Nadarajah, Saralees, and Arjun K. Gupta. Handbook of Beta Distribution and Its Applications. Taylor & Francis Group, 2004.

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Book chapters on the topic "Beta Distribution"

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Singh, Vijay P. "Beta Distribution." In Entropy-Based Parameter Estimation in Hydrology, 275–83. Dordrecht: Springer Netherlands, 1998. http://dx.doi.org/10.1007/978-94-017-1431-0_16.

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Gupta, Arjun K. "Beta Distribution." In International Encyclopedia of Statistical Science, 144–45. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_144.

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Biancardi, Enrico, Leonard W. Panella, and Robert T. Lewellen. "Range of Distribution." In Beta maritima, 75–84. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-0842-0_2.

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Frese, Lothar, and Brian Ford-Lloyd. "Range of Distribution." In Beta maritima, 49–60. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-28748-1_2.

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Posten, H. O. "Algorithms for the Beta Distribution Function." In COMPSTAT, 309–19. Heidelberg: Physica-Verlag HD, 1986. http://dx.doi.org/10.1007/978-3-642-46890-2_46.

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Baillie, D. H. "Normal Approximations to the Distribution Function of the Symmetric Beta Distribution." In Frontiers in Statistical Quality Control 5, 52–65. Heidelberg: Physica-Verlag HD, 1997. http://dx.doi.org/10.1007/978-3-642-59239-3_5.

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Thuswaldner, Jörg M. "Discrepancy Bounds for β $$\boldsymbol{\beta }$$ -adic Halton Sequences." In Number Theory – Diophantine Problems, Uniform Distribution and Applications, 423–44. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-55357-3_22.

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Salinas-Gutiérrez, Rogelio, Ángel Eduardo Muñoz-Zavala, José Antonio Guerrero-Díaz de León, and Arturo Hernández-Aguirre. "Estimation of Distribution Algorithms Based on the Beta Distribution for Bounded Search Spaces." In Advances in Soft Computing, 162–72. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-62428-0_13.

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Mameli, Valentina, and Monica Musio. "Some New Results on the Beta Skew-Normal Distribution." In Topics in Theoretical and Applied Statistics, 25–36. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-27274-0_3.

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Nakano, Takenori. "Distribution of Dust in the Disk Around Beta Pictoris." In Origin and Evolution of Interplanetary Dust, 421–24. Dordrecht: Springer Netherlands, 1991. http://dx.doi.org/10.1007/978-94-011-3640-2_86.

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Conference papers on the topic "Beta Distribution"

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Shubnikov, Eugene I. "Beta distribution and image correlation." In Second International Conference on Optical Information Processing, edited by Zhores I. Alferov, Yuri V. Gulyaev, and Dennis R. Pape. SPIE, 1996. http://dx.doi.org/10.1117/12.262577.

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Zhao Chenhao, Song Xiangdong, and Zhang Huijuan. "An asymptotic distribution of a specific beta distribution." In International Conference on Automatic Control and Artificial Intelligence (ACAI 2012). Institution of Engineering and Technology, 2012. http://dx.doi.org/10.1049/cp.2012.0998.

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Shiraishi, Hiroki, Yohei Havamizu, Hiroyuki Sato, and Keiki Takadama. "Beta Distribution based XCS Classifier System." In 2022 IEEE Congress on Evolutionary Computation (CEC). IEEE, 2022. http://dx.doi.org/10.1109/cec55065.2022.9870314.

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Janas, Z., J. Agramunt, A. Algora, L. Batist, B. A. Brown, D. Cano-Ott, R. Collatz, et al. "Beta strength distribution in neutron-deficient nuclei." In EXOTIC NUCLEI AND ATOMIC MASSES. ASCE, 1998. http://dx.doi.org/10.1063/1.57366.

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Ma, Zhanyu, and Arne Leijon. "Coding bounded support data with beta distribution." In 2010 2nd IEEE International Conference on Network Infrastructure and Digital Content (IC-NIDC 2010). IEEE, 2010. http://dx.doi.org/10.1109/icnidc.2010.5657779.

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Al-Owaisheq, Eiahl, Areeb Al-Owaisheq, and Ali El-Zaart. "A New Edge Detector Using 2D Beta Distribution." In Communication Technologies: from Theory to Applications (ICTTA). IEEE, 2008. http://dx.doi.org/10.1109/ictta.2008.4530134.

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Hristova, Hristina, Olivier Le Meur, Rémi Cozot, and Kadi Bouatouch. "Transformation of the Beta Distribution for Color Transfer." In International Conference on Computer Graphics Theory and Applications. SCITEPRESS - Science and Technology Publications, 2018. http://dx.doi.org/10.5220/0006610801120121.

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Zhao, Xin-Shuang, and Yi-Chao Cai. "Research of Weighting Method Based on Beta Distribution." In 2015 7th International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMSC). IEEE, 2015. http://dx.doi.org/10.1109/ihmsc.2015.31.

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Gullco, Robert S., and Malcolm Anderson. "The Beta Distribution and Its Application to Petrophysics." In Latin American & Caribbean Petroleum Engineering Conference. Society of Petroleum Engineers, 2007. http://dx.doi.org/10.2118/106746-ms.

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Fente, Javier, Kraig Knutson, and Cliff Schexnayder. "Defining a beta distribution function for construction simulation." In the 31st conference. New York, New York, USA: ACM Press, 1999. http://dx.doi.org/10.1145/324898.324983.

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Reports on the topic "Beta Distribution"

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Cox, Lawrence J., and Laura Casswell. MCNP(TM) Release 6.1.1 beta: Creating and Testing the Code Distribution. Office of Scientific and Technical Information (OSTI), June 2014. http://dx.doi.org/10.2172/1134769.

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Nelson, W. R., and J. Liu. Sampling the fermi distribution for {beta}-decay energy input to EGS4. Office of Scientific and Technical Information (OSTI), June 1992. http://dx.doi.org/10.2172/10165423.

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Nelson, W. R., and J. Liu. Sampling the fermi distribution for. beta. -decay energy input to EGS4. Office of Scientific and Technical Information (OSTI), June 1992. http://dx.doi.org/10.2172/7234613.

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Didonato, Armido. An Inverse of the Incomplete Beta Function (F-(Variance Ratio) Distribution Function). Fort Belvoir, VA: Defense Technical Information Center, August 2005. http://dx.doi.org/10.21236/ada467901.

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Acosta, D. Measurement of the Moments of the Hadronic Invariant Mass Distribution in Semileptonic Beta Decays. Office of Scientific and Technical Information (OSTI), March 2005. http://dx.doi.org/10.2172/842931.

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Meot, F., and A. Paris. Concerning effects of low-beta region fringe fields and multipole error distribution on dynamics in LHC. Office of Scientific and Technical Information (OSTI), August 1997. http://dx.doi.org/10.2172/573872.

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Kaplan, P. G. Modeling the uncertainties in the parameter values of a sparse data set using the beta probability distribution. Office of Scientific and Technical Information (OSTI), December 1989. http://dx.doi.org/10.2172/137635.

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Hwang, D. Q., R. D. Horton, and R. Evans. Diagnostic of the spatial and velocity distribution of alpha particles in tokamak fusion reactor using beat-wave generated lower hybrid wave. Progress report, 1994--1995. Office of Scientific and Technical Information (OSTI), March 1995. http://dx.doi.org/10.2172/584955.

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Hwang, D. Q., R. D. Horton, and R. W. Evans. Final Report (1994 to 1996) Diagnostic of the Spatial and Velocity Distribution of Alpha Particles in Tokamak Fusion Reactor using Beat-wave Generated Lower Hybrid Wave. Office of Scientific and Technical Information (OSTI), June 1999. http://dx.doi.org/10.2172/762752.

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Blum, Abraham, Henry T. Nguyen, and N. Y. Klueva. The Genetics of Heat Shock Proteins in Wheat in Relation to Heat Tolerance and Yield. United States Department of Agriculture, August 1993. http://dx.doi.org/10.32747/1993.7568105.bard.

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Abstract:
Fifty six diverse spring wheat cultivars were evaluated for genetic variation and heritability for thermotolerance in terms of cell-membrane stability (CMS) and triphenyl tetrazolium chloride (TTC) reduction. The most divergent cultivars for thermotolerance (Danbata-tolerant and Nacozari-susceptible) were crossed to develop an F8 random onbred line (RIL) population. This population was evaluated for co-segragation in CMS, yield under heat stress and HSP accumulation. Further studies of thermotolerance in relations to HSP and the expression of heterosis for growth under heat stress were performed with F1 hybrids of wheat and their parental cultivars. CMS in 95 RILs ranged from 76.5% to 22.4% with 71.5% and 31.3% in Danbata and Nacozari, respectively. The population segregated with a normal distribution across the full range of the parental values. Yield and biomass under non-stress conditions during the normal winter season at Bet Dagan dit not differ between the two parental cultivar, but the range of segregation for these traits in 138 RILs was very high and distinctly transgressive with a CV of 35.3% and 42.4% among lines for biomass and yield, respectively. Mean biomass and yield of the population was reduced about twofold when grown under the hot summer conditions (irrigated) at Bet Dagan. Segregation for biomass and yield was decreased relative to the normal winter conditions with CV of 20.2% and 23.3% among lines for biomass and yield, respectively. However, contrary to non-stress conditions, the parental cultivars differed about twofold in biomass and yield under heat stress and the population segregated with normal distribution across the full range of this difference. CMS was highly and positively correlated across 79 RILs with biomass (r=0.62**) and yield (r=0.58**) under heat stress. No such correlation was obtained under the normal winter conditions. All RILs expressed a set of HSPs under heat shock (37oC for 2 h). No variation was detected among RILs in high molecular weight HSP isoforms and they were similar to the patterns of the parental cultivars. There was a surprisingly low variability in low molecular weight HSP isoforms. Only one low molecular weight and Nacozari-specific HSP isoform (belonging to HSP 16.9 family) appeared to segregate among all RILs, but it was not quantitatively correlated with any parameter of plant production under heat stress or with CMS in this population. It is concluded that this Danbata/Nacozari F8 RIL population co-segregated well for thermotolerance and yield under heat stress and that CMS could predict the relative productivity of lines under chronic heat stress. Regretfully this population did not express meaningful variability for HSP accumulation under heat shock and therefore no role could be seen for HSP in the heat tolerance of this population. In the study of seven F1 hybrids and their parent cultivars it was found that heterosis (superiority of the F1 over the best parent) for CMs was generally lower than that for growth under heat stress. Hybrids varied in the rate of heterosis for growth at normal (15o/25o) and at high (25o/35o) temperatures. In certain hybrids heterosis for growth significantly increased at high temperature as compared with normal temperature, suggesting temperature-dependent heterosis. Generally, under normal temperature, only limited qualitative variation was detected in the patterns of protein synthesis in four wheat hybrids and their parents. However, a singular protein (C47/5.88) was specifically expressed only in the most heterotic hybrid at normal temperature but not in its parent cultivars. Parental cultivars were significantly different in the sets of synthesized HSP at 37o. No qualitative changes in the patterns of protein expression under heat stress were correlated with heterosis. However, a quantitative increase in certain low molecular weight HSP (mainly H14/5.5 and H14.5.6, belonging to the HSP16.9 family) was positively associated with greater heterosis for growth at high temperature. None of these proteins were correlated with CMS across hybrids. These results support the concept of temperature-dependent heterosis for growth and a possible role for HSP 16.9 family in this respect. Finally, when all experiments are viewed together, it is encouraging to find that genetic variation in wheat yield under chronic heat stress is associated with and well predicted by CMS as an assay of thermotolerance. On the other hand the results for HSP are elusive. While very low genetic variation was expressed for HSP in the RIL population, a unique low molecular weight HSP (of the HSP 16.9 family) could be associated with temperature dependant heterosis for growth.
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