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Journal articles on the topic 'Non-informative priors'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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1

Shemyakin, Arkady. "Hellinger Distance and Non-informative Priors." Bayesian Analysis 9, no. 4 (2014): 923–38. http://dx.doi.org/10.1214/14-ba881.

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2

Guglielmi, Alessandra, and Eugenio Melilli. "Non-informative invariant priors yield peculiar marginals." Communications in Statistics - Theory and Methods 27, no. 9 (1998): 2293–306. http://dx.doi.org/10.1080/03610929808832228.

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3

Lira, Ignacio, and Dieter Grientschnig. "Non-informative priors in GUM Supplement 1." Measurement 44, no. 9 (2011): 1790–91. http://dx.doi.org/10.1016/j.measurement.2011.05.020.

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4

ناصر, جنان عباس. "A Comparison of Bayes Estimators for the parameter of Rayleigh Distribution with Simulation." Journal of Economics and Administrative Sciences 24, no. 106 (2018): 49. http://dx.doi.org/10.33095/jeas.v24i106.41.

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A comparison of double informative and non- informative priors assumed for the parameter of Rayleigh distribution is considered. Three different sets of double priors are included, for a single unknown parameter of Rayleigh distribution. We have assumed three double priors: the square root inverted gamma (SRIG) - the natural conjugate family of priors distribution, the square root inverted gamma – the non-informative distribution, and the natural conjugate family of priors - the non-informative distribution as double priors .The data is generating form three cases from Rayleigh distribution fo

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5

CHANDRA, N., and V. K. RATHAUR. "Bayesian Estimation of Augmented Exponential Strength Reliability Models Under Non-informative Priors." Mathematical Journal of Interdisciplinary Sciences 5, no. 1 (2016): 15–31. http://dx.doi.org/10.15415/mjis.2016.51002.

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6

Kim, Dal Ho, Woo Dong Lee, and Sang Gil Kang. "Non-informative priors for the inverse Weibull distribution." Journal of Statistical Computation and Simulation 84, no. 5 (2012): 1039–54. http://dx.doi.org/10.1080/00949655.2012.739171.

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7

Dette, Holger, Christophe Ley, and Francisco Rubio. "Natural (Non-)Informative Priors for Skew-symmetric Distributions." Scandinavian Journal of Statistics 45, no. 2 (2017): 405–20. http://dx.doi.org/10.1111/sjos.12306.

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8

Dawid, A. P. "Comments on “non-informative priors do not exist”." Journal of Statistical Planning and Inference 65, no. 1 (1997): 178–80. http://dx.doi.org/10.1016/s0378-3758(97)90069-0.

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9

Naqash, Saima, S. P. Ahmad, and Aquil Ahmed. "Bayesian Approach to Generalized Normal Distribution under Non-Informative and Informative Priors." International Journal of Mathematical Sciences and Computing 4, no. 4 (2018): 19–33. http://dx.doi.org/10.5815/ijmsc.2018.04.02.

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10

Lindley, Dennis. "Some comments on “non-informative priors do not exist”." Journal of Statistical Planning and Inference 65, no. 1 (1997): 182–84. http://dx.doi.org/10.1016/s0378-3758(97)90073-2.

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11

Shakhatreh, Mohammed K., and Mohammad A. Aljarrah. "Bayesian Analysis of Unit Log-Logistic Distribution Using Non-Informative Priors." Mathematics 11, no. 24 (2023): 4947. http://dx.doi.org/10.3390/math11244947.

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The unit log-logistic distribution is a suitable choice for modeling data enclosed within the unit interval. In this paper, estimating the parameters of the unit-log-logistic distribution is performed through a Bayesian approach with non-informative priors. Specifically, we use Jeffreys, reference, and matching priors, with the latter depending on the interest parameter. We derive the corresponding posterior distributions and validate their propriety. The Bayes estimators are then computed using Markov Chain Monte Carlo techniques. To assess the finite sample performance of these Bayes estimat

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12

Sadoun, Ahmed, Imen Ouchen, and Farouk Metiri. "Bayesian Premium Estimators for NXLindley Model Under Different Loss Functions." Statistics, Optimization & Information Computing 13, no. 6 (2025): 2647–68. https://doi.org/10.19139/soic-2310-5070-2442.

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The conditional distribution of (X|θ) is regarded as the NXLindley distribution. This study is centered on the estimation of the Bayesian premium using the symmetric squared error loss function and the asymmetric Linex loss function, employing the extension of Jeffreys as non-informative priors and Gamma prior as informative priors. Owing to its complexity and lack of linearity, we rely on a numerical approximation for establishing the Bayesian premium. A simulation and comparison study with several sample sizes is presented.

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13

Chang, In Hong, and Byung Hwee Kim. "Non-informative priors in the generalized gamma stress–strength systems." IIE Transactions 43, no. 11 (2011): 797–804. http://dx.doi.org/10.1080/0740817x.2011.590439.

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14

Ghosh, J. K. "Non-informative priors do not exist — discussion of a discussion." Journal of Statistical Planning and Inference 65, no. 1 (1997): 180–82. http://dx.doi.org/10.1016/s0378-3758(97)90071-9.

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15

Singh, Sanjay Kumar, Umesh Singh, and Abhimanyu Singh Yadav. "Reliability estimation and prediction for extension of exponential distribution using informative and non-informative priors." International Journal of System Assurance Engineering and Management 6, no. 4 (2014): 466–78. http://dx.doi.org/10.1007/s13198-014-0299-1.

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16

Awad, Manahel Kh, and Huda A. Rasheed. "Estimation of the Reliability Function of Basic Gompertz Distribution under Different Priors." Ibn AL- Haitham Journal For Pure and Applied Sciences 33, no. 3 (2020): 167. http://dx.doi.org/10.30526/33.3.2482.

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In this paper, some estimators for the reliability function R(t) of Basic Gompertz (BG) distribution have been obtained, such as Maximum likelihood estimator, and Bayesian estimators under General Entropy loss function by assuming non-informative prior by using Jefferys prior and informative prior represented by Gamma and inverted Levy priors. Monte-Carlo simulation is conducted to compare the performance of all estimates of the R(t), based on integrated mean squared.

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17

Vaidogas, Egidijus Rytas. "Bayesian Processing of Data on Bursts of Pressure Vessels." Information Technology and Control 50, no. 4 (2021): 607–26. http://dx.doi.org/10.5755/j01.itc.50.4.29690.

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Two alternative Bayesian approaches are proposed for the prediction of fragmentation of pressure vessels triggered off by accidental explosions (bursts) of these containment structures. It is shown how to carry out this prediction with post-mortem data on fragment numbers counted after past explosion accidents. Results of the prediction are estimates of probabilities of individual fragment numbers. These estimates are expressed by means of Bayesian prior or posterior distributions. It is demonstrated how to elicit the prior distributions from relatively scarce post-mortem data on vessel fragme

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18

Ferreira, Thales Rangel, Luiz Alberto Beijo, Gilberto Rodrigues Liska, and Giulia Eduarda Bento. "MODELAGEM BAYESIANA DA TEMPERATURA MÁXIMA DO AR EM DIVINÓPOLIS-MG." Nativa 12, no. 3 (2024): 449–56. http://dx.doi.org/10.31413/nat.v12i3.17665.

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Nesta pesquisa, objetivou-se modelar o comportamento da temperatura máxima trimestral da cidade de Divinópolis-MG, ajustando a distribuição Generalizada de Valores Extremos às séries históricas de temperaturas máximas através de dois métodos distintos: Máxima Verossimilhança (MV) e Inferência Bayesiana. Objetivou-se também, para cada tempo de retorno, calcular os níveis de retorno de temperatura máxima da referida localidade, avaliando a acurácia e o erro médio de predição (EMP). Para o cálculo dos níveis de retorno foram utilizados o método de MV e abordagens Bayesianas utilizando diferentes

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19

Ateeq, Kahkashan, Noumana Safdar, and Shakeel Ahmed. "Exploring the Exponentiated Transmuted Inverse Rayleigh Distribution (ETIRD) in Classical and Bayesian Paradigms." STATISTICS, COMPUTING AND INTERDISCIPLINARY RESEARCH 4, no. 2 (2022): 17–37. http://dx.doi.org/10.52700/scir.v4i2.114.

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We derived, a new three parameters continuous probability distribution called Exponentiated Transmuted Inverse Rayleigh Distribution (ETIRD). Various mathematical properties of the new distribution including mean, rth moments, moment generating function, quantile function etc. are derived. In the Classical paradigm, the estimators of the distribution are obtained using the maximum likelihood method. The Bayes estimators are derived under square error loss function (SELF) using non-informative and informative priors via the Lindley approximation technique. Bayes Estimators are compared with the

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20

Sultana, Tabasam, Muhammad Aslam, and Mariya Raftab. "Bayesian estimation of 3-component mixture of Gumbel type-II distributions under non-informative and informative priors." Journal of the National Science Foundation of Sri Lanka 45, no. 3 (2017): 287. http://dx.doi.org/10.4038/jnsfsr.v45i3.8193.

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21

Irony, Telba Z., and Nozer D. Singpurwalla. "Non-informative priors do not exist A dialogue with José M. Bernardo." Journal of Statistical Planning and Inference 65, no. 1 (1997): 159–77. http://dx.doi.org/10.1016/s0378-3758(97)00074-8.

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22

Burghaus, Ina, and Holger Dette. "Optimal designs for nonlinear regression models with respect to non-informative priors." Journal of Statistical Planning and Inference 154 (November 2014): 12–25. http://dx.doi.org/10.1016/j.jspi.2014.05.009.

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23

Arora, Sangeeta, Kalpana K. Mahajan, and Vikas Jangra. "A Bayesian estimation of the Gini index and the Bonferroni index for the Dagum distribution with the application of different priors." Statistics in Transition New Series 23, no. 2 (2022): 49–68. http://dx.doi.org/10.2478/stattrans-2022-0016.

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Abstract Bayesian estimators and highest posterior density credible intervals are obtained for two popular inequality measures, viz. the Gini index and the Bonferroni index in the case of the Dagum distribution. The study considers informative and non-informative priors, i.e. the Mukherjee-Islam prior and the extension of Jeffrey’s prior, respectively, under the presumption of the Linear Exponential (LINEX) loss function. A Monte Carlo simulation study is carried out in order to obtain the relative efficiency of both the Gini and Bonferroni indices while taking into consideration different pri

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24

Pham, Huong T. T., and Hoa Pham. "On the existence of posterior mean for Bayesian logistic regression." Monte Carlo Methods and Applications 27, no. 3 (2021): 277–88. http://dx.doi.org/10.1515/mcma-2021-2089.

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Abstract Existence conditions for posterior mean of Bayesian logistic regression depend on both chosen prior distributions and a likelihood function. In logistic regression, different patterns of data points can lead to finite maximum likelihood estimates (MLE) or infinite MLE of the regression coefficients. Albert and Anderson [On the existence of maximum likelihood estimates in logistic regression models, Biometrika 71 1984, 1, 1–10] gave definitions of different types of data points, which are complete separation, quasicomplete separation and overlap. Conditions for the existence of the MLE

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25

Robnik, Jakob, and Uroš Seljak. "Statistical Significance Testing for Mixed Priors: A Combined Bayesian and Frequentist Analysis." Entropy 24, no. 10 (2022): 1328. http://dx.doi.org/10.3390/e24101328.

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In many hypothesis testing applications, we have mixed priors, with well-motivated informative priors for some parameters but not for others. The Bayesian methodology uses the Bayes factor and is helpful for the informative priors, as it incorporates Occam’s razor via the multiplicity or trials factor in the look-elsewhere effect. However, if the prior is not known completely, the frequentist hypothesis test via the false-positive rate is a better approach, as it is less sensitive to the prior choice. We argue that when only partial prior information is available, it is best to combine the two

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26

Chandra, N., and V. K. Rathaur. "On estimation of augmented strength reliability parameters under non-informative priors: A non-identical case." Model Assisted Statistics and Applications 12, no. 2 (2017): 137–50. http://dx.doi.org/10.3233/mas-170390.

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27

Singh, Sanjay Kumar, Umesh Singh, and Dinesh Kumar. "Bayes estimators of the reliability function and parameter of inverted exponential distribution using informative and non-informative priors." Journal of Statistical Computation and Simulation 83, no. 12 (2013): 2258–69. http://dx.doi.org/10.1080/00949655.2012.690156.

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28

Braun, Robin. "The importance of supply and demand for oil prices: Evidence from non‐Gaussianity." Quantitative Economics 14, no. 4 (2023): 1163–98. http://dx.doi.org/10.3982/qe2091.

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When quantifying the importance of supply and demand for oil price fluctuations, a wide range of estimates have been reported. Models identified via a sharp upper bound on the short‐run price elasticity of supply find supply shocks to be minor drivers. In turn, when replacing the upper bound with a weakly informative prior, supply shocks turn out to be substantially more important. In this paper, I revisit the evidence in a model that combines weakly informative priors with identification by non‐Gaussianity. For this purpose, a SVAR is developed where the unknown distributions of the structura

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29

Conigliani, Caterina. "A Bayesian model averaging approach with non-informative priors for cost-effectiveness analyses." Statistics in Medicine 29, no. 16 (2010): 1696–709. http://dx.doi.org/10.1002/sim.3901.

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30

Xie, Longfei, Fengri Li, Lianjun Zhang, Faris Rafi Almay Widagdo, and Lihu Dong. "A Bayesian Approach to Estimating Seemingly Unrelated Regression for Tree Biomass Model Systems." Forests 11, no. 12 (2020): 1302. http://dx.doi.org/10.3390/f11121302.

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Accurate estimation of tree biomass is required for accounting for and monitoring forest carbon stocking. Allometric biomass equations constructed by classical statistical methods are widely used to predict tree biomass in forest ecosystems. In this study, a Bayesian approach was proposed and applied to develop two additive biomass model systems: one with tree diameter at breast height as the only predictor and the other with both tree diameter and total height as the predictors for planted Korean larch (Larix olgensis Henry) in the Northeast, P.R. China. The seemingly unrelated regression (SU

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31

Abu-Moussa, Mahmoud Hamed, Najwan Alsadat, and Ali Sharawy. "On Estimation of Reliability Functions for the Extended Rayleigh Distribution under Progressive First-Failure Censoring Model." Axioms 12, no. 7 (2023): 680. http://dx.doi.org/10.3390/axioms12070680.

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When conducting reliability studies, the progressive first-failure censoring (PFFC) method is useful in situations in which the units of the life testing experiment are separated into groups consisting of k units each with the intention of seeing only the first failure in each group. Using progressive first-failure censored samples, the statistical inference for the parameters, reliability, and hazard functions of the extended Rayleigh distribution (ERD) are investigated in this study. The asymptotic normality theory of maximum likelihood estimates (MLEs) is used in order to acquire the maximu

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32

Najmaldin, Dler, Mahmut Kara, Yıldırım Demir, and Sakir İşleyen. "Bayesian Analysis of Two Parameter Weibull Distribution Using Different Loss Functions." Indonesian Journal of Data and Science 5, no. 3 (2024): 178–89. https://doi.org/10.56705/ijodas.v5i3.179.

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This paper focuses on the Bayesian technique to estimate the parameters of the Weibull distribution. At this location, we use both informative and non-informative priors. We calculate the estimators and their posterior risks using different asymmetric and symmetric loss functions. Bayes estimators do not have a closed form under these loss functions. Therefore, we use an approximation approach established by Lindley to get the Bayes estimates. A comparative analysis is conducted to compare the suggested estimators using Monte Carlo simulation based on the related posterior risk. We also analyz

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33

Sultana, Tabasam, and Muhammad Aslam. "A 3-component mixture of inverse Rayleigh distributions: properties and estimation in Bayesian framework." International Journal of Basic and Applied Sciences 5, no. 2 (2016): 120. http://dx.doi.org/10.14419/ijbas.v5i2.5935.

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<p>This paper is about studying a 3-component mixture of the inverse Rayleigh distributions under Bayesian perspective. The censored sampling scheme is considered due to its popularity in reliability theory and survival analysis. The expressions for the Bayes estimators and their posterior risks are derived under different loss scenarios. In case, no little prior information is available, elicitation of hyper parameters is given. To examine, numerically, the performance of the Bayes estimators using non-informative and informative priors under different loss functions, we have simulated

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34

Alotaibi, Refah, H. Rezk, and Sanku Dey. "MCMC Method for Exponentiated Lomax Distribution based on Accelerated Life Testing with Type I Censoring." WSEAS TRANSACTIONS ON MATHEMATICS 20 (July 5, 2021): 319–34. http://dx.doi.org/10.37394/23206.2021.20.33.

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Accelerated Life Testing (ALT) is an effective technique which has been used in different fields to obtain more failures in a shorter period of time. It is more economical than traditional reliability testing. In this article, we propose Bayesian inference approach for planning optimal constant stress ALT with Type I censoring. The lifetime of a test unit follows an exponentiated Lomax distribution. Bayes point estimates of the model parameters and credible intervals under uniform and log-normal priors are obtained. Besides, optimum test plan based on constant stress ALT under Type I censoring

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35

Georgiou, Christina, Sean Anderson, and Tony Dodd. "Constructing informative Bayesian map priors: A multi-objective optimisation approach applied to indoor occupancy grid mapping." International Journal of Robotics Research 36, no. 3 (2017): 274–91. http://dx.doi.org/10.1177/0278364916687027.

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The problem of simultaneous localisation and mapping (SLAM) has been addressed in numerous ways with different approaches aiming to produce faster, more robust solutions that yield consistent maps. This focus, however, has resulted in a number of solutions that perform poorly in challenging real life scenarios. In order to achieve improved performance and map quality this article proposes a novel method to construct informative Bayesian mapping priors through a multi-objective optimisation of prior map design variables defined using a source of prior information. This concept is explored for 2

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36

Naser Al-obedy, Jinan Abbas. "The Bayesian Estimation for The Shape Parameter of The Power Function Distribution (PFD-I) to Use Hyper Prior Functions." Journal of Economics and Administrative Sciences 27, no. 127 (2021): 229–52. http://dx.doi.org/10.33095/jeas.v27i127.2146.

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The objective of this study is to examine the properties of Bayes estimators of the shape parameter of the Power Function Distribution (PFD-I), by using two different prior distributions for the parameter θ and different loss functions that were compared with the maximum likelihood estimators. In many practical applications, we may have two different prior information about the prior distribution for the shape parameter of the Power Function Distribution, which influences the parameter estimation. So, we used two different kinds of conjugate priors of shape parameter θ of the Power Function Di

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37

Al-obedy, Jinan A. Naser. "Posterior Estimates for the Parameter of the Poisson Distribution by Using Two Different Loss Functions." Ibn AL- Haitham Journal For Pure and Applied Sciences 35, no. 1 (2022): 60–72. http://dx.doi.org/10.30526/35.1.2800.

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In this paper, Bayes estimators of Poisson distribution have been derived by using two loss functions: the squared error loss function and the proposed exponential loss function in this study, based on different priors classified as the two different informative prior distributions represented by erlang and inverse levy prior distributions and non-informative prior for the shape parameter of Poisson distribution. The maximum likelihood estimator (MLE) of the Poisson distribution has also been derived. A simulation study has been fulfilled to compare the accuracy of the Bayes estimates with the

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38

Yanuar, Ferra, Hazmira Yozza, and Ratna Vrima Rescha. "Comparison of Two Priors in Bayesian Estimation for Parameter of Weibull Distribution." Science and Technology Indonesia 4, no. 3 (2019): 82. http://dx.doi.org/10.26554/sti.2019.4.3.82-87.

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This present study purposes to conduct Bayesian inference for scale parameters, denoted by , from Weibull distribution. The prior distribution chosen in this study is the prior conjugate, that is inverse gamma and non-informative prior, namely Jeffreys’ prior. This research also aims to study several theoretical properties of posterior distribution based on prior used and then implement it to generated data and make comparison between both Bayes estimator as well. The method used to evaluate the best estimator is based on the smallest Mean Square Error (MSE). This study proved that Bayes estim

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39

Gilani, Ghausia Masood, and Nasir Abbas. "BAYESIAN ANALYSIS FOR THE PAIRED COMPARISON MODEL WITH ORDER EFFECTS (USING NON-INFORMATIVE PRIORS)." Pakistan Journal of Statistics and Operation Research 4, no. 2 (2008): 83. http://dx.doi.org/10.18187/pjsor.v4i2.52.

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40

Chandra, N., and V. K. Rathaur. "Inferences on Non-Identical Stress and Generalized Augmented Strength Reliability Parameters Under Informative Priors." International Journal of Reliability, Quality and Safety Engineering 27, no. 04 (2020): 2050014. http://dx.doi.org/10.1142/s021853932050014x.

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In this paper, an attempt has been made to estimate the augmented strength reliability of a system for the generalized case of Augmentation Strategy Plan (ASP) by assuming that the strength [Formula: see text] and common stress [Formula: see text] are independently but not identically distributed as gamma distribution with parameters [Formula: see text] and [Formula: see text], respectively. ASP deals with two important challenges (i) early failures in a newly manufactured system while first and subsequent use and (ii) frequent failures of used system. ASP has a significant role in enhancing t

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41

Cheema, Ammara Nawaz, and Muhammad Aslam. "Bayesian analysis for 3-component mixture of exponentiated Weibull distribution assuming non-informative priors." Journal of Statistical Computation and Simulation 90, no. 4 (2019): 586–605. http://dx.doi.org/10.1080/00949655.2019.1692840.

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42

Wüthrich, Mario V. "Challenges with non-informative gamma priors in the Bayesian over-dispersed Poisson reserving model." Insurance: Mathematics and Economics 52, no. 2 (2013): 352–58. http://dx.doi.org/10.1016/j.insmatheco.2013.02.002.

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43

Tsai, Tzong-Ru, Yuhlong Lio, Jyun-You Chiang, and Ya-Wen Chang. "Stress–Strength Inference on the Multicomponent Model Based on Generalized Exponential Distributions under Type-I Hybrid Censoring." Mathematics 11, no. 5 (2023): 1249. http://dx.doi.org/10.3390/math11051249.

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The stress–strength analysis is investigated for a multicomponent system, where all strength variables of components follow a generalized exponential distribution and are subject to the generalized exponential distributed stress. The estimation methods of the maximum likelihood and Bayesian are utilized to infer the system reliability. For the Bayesian estimation method, informative and non-informative priors combined with three loss functions are considered. Because the computational difficulty on working posteriors, the Markov chain Monte Carlo method is adopted to obtain the approximation o

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44

Tahir, Muhammad, and Muhammad Aslam. "Bayesian Analysis of the 3-Component Mixture of Exponential Distribution Assuming the Non-Informative Priors." Revista Colombiana de Estadística 38, no. 2 (2015): 431–52. http://dx.doi.org/10.15446/rce.v38n2.51670.

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Bayesian analysis of the 3-component mixture of an Exponential distribution under type-I right censoring scheme is considered in this paper. The Bayes estimators and posterior risks for the unknown parameters are derived under squared error loss function, precautionary loss function and DeGroot loss function assuming the non-informative (uniform and Jeffreys') priors. The Bayes estimators and posterior risks are viewed as a function of the test termination time. A simulation study is given to highlight and compare the properties of the Bayes estimates.

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45

Nagy, M., M. E. Bakr, and Adel Fahad Alrasheedi. "Analysis with Applications of the Generalized Type-II Progressive Hybrid Censoring Sample from Burr Type-XII Model." Mathematical Problems in Engineering 2022 (February 18, 2022): 1–21. http://dx.doi.org/10.1155/2022/1241303.

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In this article, based on the generalized Type-II progressive hybrid censoring sample from the Burr Type-XII distribution, maximum likelihood and Bayesian inference are discussed. Point and interval estimates of unknown parameters, reliability, and hazard functions are developed. We employed several loss functions, such as squared error, LINEX, and general entropy, as symmetric and asymmetric loss functions and various prior distributions as informative and non-informative priors for Bayesian inference of unknown parameters. Under a generalized Type-II progressive hybrid censoring sample, we a

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46

Journal, Baghdad Science. "Comparison of Maximum Likelihood and some Bayes Estimators for Maxwell Distribution based on Non-informative Priors." Baghdad Science Journal 10, no. 2 (2013): 480–88. http://dx.doi.org/10.21123/bsj.10.2.480-488.

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In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The eff

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47

Al-Baldawi, Tasnim H. K. "Comparison of Maximum Likelihood and some Bayes Estimators for Maxwell Distribution based on Non-informative Priors." Baghdad Science Journal 10, no. 2 (2013): 480–88. http://dx.doi.org/10.21123/bsj.2013.10.2.480-488.

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In this paper, Bayes estimators of the parameter of Maxwell distribution have been derived along with maximum likelihood estimator. The non-informative priors; Jeffreys and the extension of Jeffreys prior information has been considered under two different loss functions, the squared error loss function and the modified squared error loss function for comparison purpose. A simulation study has been developed in order to gain an insight into the performance on small, moderate and large samples. The performance of these estimators has been explored numerically under different conditions. The eff

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48

Ehtasham Ahmed Zahoor and Syeda Arjumand Javaid. "Bayesian Look at The Rare Event Distribution." Kashmir Journal of Science 2, no. 1 (2023): 43–59. https://doi.org/10.63147/krjs.v2i1.21.

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In this article, Bayesian analysis of parameter () of Poisson distribution under simulated data is conducted. Posterior distributions are obtained under two informative (Gamma and Exponential) and two non-informative (Uniform and Jaffrey’s) priors. Five loss functions including Square Error Loss Function (SELF), Weighted Square Error Loss Function (WSELF), LINEX Loss Function (LLF), Quasi Quadratic Loss Function (QQLF) and Precautionary Loss Function (PLF) are used to obtain the Bayes estimators and risks associated with them to study the performance and behavior of the Poisson parameter (). F

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Chandra, N., and V. K. Rathaur. "Bayes Estimation of Augmenting Gamma Strength Reliability of a System under Non-informative Prior Distributions." Calcutta Statistical Association Bulletin 69, no. 1 (2017): 87–102. http://dx.doi.org/10.1177/0008068317696574.

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In this article, Bayes estimation of system’s augmented strength reliability is studied under squared-error loss function (SELF) and LINEX loss function (LLF) for the generalized case of augmentation strategy plan (ASP). ASP is helpful in enhancing the strength reliability of weaker system/equipment. It is assumed that the stress (usual) and augmented strength follow a gamma distribution with common shape [Formula: see text] and scale [Formula: see text] parameters. A simulation study is performed for the comparisons of Bayes estimators of augmented strength reliability for non-informative typ

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Bodnar, Olha, and Taras Bodnar. "Practical aspects of Bayesian multivariate meta-analysis." Ukrainian Metrological Journal, no. 4 (December 29, 2022): 7–11. http://dx.doi.org/10.24027/2306-7039.4.2022.276300.

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Multivariate meta-analysis is a mostly used approach when multivariate results of several studies are pooled together. The multivariate model of random effects provides a tool to perform the multivariate meta-analysis in practice. In this paper, we discuss Bayesian inference procedures derived for the multivariate model of random effects when the model parameters are endowed with two non-informative priors: the Berger-Bernardo reference prior and the Jeffreys prior. Moreover, two Metropolis-Hastings algorithms are presented, and their convergence properties are analysed via simulations.

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