Relevant bibliographies by topics / Gibbs-type priors

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Academic literature on the topic 'Gibbs-type priors'

Author: Grafiati
Published: 9 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "Gibbs-type priors"

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Bacallado, S., M. Battiston, S. Favaro, and L. Trippa. "Sufficientness Postulates for Gibbs-Type Priors and Hierarchical Generalizations." Statistical Science 32, no. 4 (2017): 487–500. http://dx.doi.org/10.1214/17-sts619.

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De Blasi, Pierpaolo, Stefano Favaro, Antonio Lijoi, Ramses H. Mena, Igor Prunster, and Matteo Ruggiero. "Are Gibbs-Type Priors the Most Natural Generalization of the Dirichlet Process?" IEEE Transactions on Pattern Analysis and Machine Intelligence 37, no. 2 (2015): 212–29. http://dx.doi.org/10.1109/tpami.2013.217.

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Favaro, Stefano, and Lancelot F. James. "A note on nonparametric inference for species variety with Gibbs-type priors." Electronic Journal of Statistics 9, no. 2 (2015): 2884–902. http://dx.doi.org/10.1214/15-ejs1096.

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Jayalath, Kalanka P. "Improved Bayesian Inferences for Right-Censored Birnbaum–Saunders Data." Mathematics 12, no. 6 (2024): 874. http://dx.doi.org/10.3390/math12060874.

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This work focuses on making Bayesian inferences for the two-parameter Birnbaum–Saunders (BS) distribution in the presence of right-censored data. A flexible Gibbs sampler is employed to handle the censored BS data in this Bayesian work that relies on Jeffrey’s and Achcar’s reference priors. A comprehensive simulation study is conducted to compare estimates under various parameter settings, sample sizes, and levels of censoring. Further comparisons are drawn with real-world examples involving Type-II, progressively Type-II, and randomly right-censored data. The study concludes that the suggeste
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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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Wang, Liang, Sanku Dey, and Yogesh Mani Tripathi. "Classical and Bayesian Inference of the Inverse Nakagami Distribution Based on Progressive Type-II Censored Samples." Mathematics 10, no. 12 (2022): 2137. http://dx.doi.org/10.3390/math10122137.

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This paper explores statistical inferences when the lifetime of product follows the inverse Nakagami distribution using progressive Type-II censored data. Likelihood-based and maximum product of spacing (MPS)-based methods are considered for estimating the parameters of the model. In addition, approximate confidence intervals are constructed via the asymptotic theory using both likelihood and product spacing functions. Based on traditional likelihood and the product of spacing functions, Bayesian estimates are also considered under a squared error loss function using non-informative priors, an
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Kazempoor, Jaber, Arezou Habibirad, Adel Ahmadi Nadi, and Gholam Reza Mohtashami Borzadaran. "Statistical inferences for the Weibull distribution under adaptive progressive type-II censoring plan and their application in wind speed data analysis." Statistics, Optimization & Information Computing 11, no. 4 (2023): 829–52. http://dx.doi.org/10.19139/soic-2310-5070-1501.

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This paper provides four well-known statistical inferences for the principal parameters regarding the two-parameter Weibull distribution including its hazard, quantile, and survival function based on an adaptive progressive type-II censoring plan. The statistical inferences involve the likelihood and approximate likelihood methods, the Bayesian approach, the bootstrap procedure, and a new conditional technique. To construct Bayesian point estimators and credible intervals, Markov chain Monte Carlo, Metropolis-Hastings, and Gibbs sampling algorithms were used. The Bayesian estimators are develo
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Bassetti, Federico, and Lucia Ladelli. "Mixture of Species Sampling Models." Mathematics 9, no. 23 (2021): 3127. http://dx.doi.org/10.3390/math9233127.

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We introduce mixtures of species sampling sequences (mSSS) and discuss how these sequences are related to various types of Bayesian models. As a particular case, we recover species sampling sequences with general (not necessarily diffuse) base measures. These models include some “spike-and-slab” non-parametric priors recently introduced to provide sparsity. Furthermore, we show how mSSS arise while considering hierarchical species sampling random probabilities (e.g., the hierarchical Dirichlet process). Extending previous results, we prove that mSSS are obtained by assigning the values of an e
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Elshahhat, Ahmed, Ritwik Bhattacharya, and Heba S. Mohammed. "Survival Analysis of Type-II Lehmann Fréchet Parameters via Progressive Type-II Censoring with Applications." Axioms 11, no. 12 (2022): 700. http://dx.doi.org/10.3390/axioms11120700.

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A new three-parameter Type-II Lehmann Fréchet distribution (LFD-TII), as a reparameterized version of the Kumaraswamy–Fréchet distribution, is considered. In this study, using progressive Type-II censoring, different estimation methods of the LFD-TII parameters and its lifetime functions, namely, reliability and hazard functions, are considered. In a frequentist setup, both the likelihood and product of the spacing estimators of the considered parameters are obtained utilizing the Newton–Raphson method. From the normality property of the proposed classical estimators, based on Fisher’s informa
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Feroze, Navid, Ali Al-Alwan, Muhammad Noor-ul-Amin, Shajib Ali, and R. Alshenawy. "Bayesian Estimation for the Doubly Censored Topp Leone Distribution using Approximate Methods and Fuzzy Type of Priors." Journal of Function Spaces 2022 (March 19, 2022): 1–15. http://dx.doi.org/10.1155/2022/4816748.

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The Topp Leone distribution (TLD) is a lifetime model having finite support and U-shaped hazard rate; these features distinguish it from the famous lifetime models such as gamma, Weibull, or Log-normal distribution. The Bayesian methods are very much linked to the Fuzzy sets. The Fuzzy priors can be used as prior information in the Bayesian models. This paper considers the posterior analysis of TLD, when the samples are doubly censored. The independent informative priors (IPs) which are very close to the Fuzzy priors have been proposed for the analysis. The symmetric and asymmetric loss functi
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Dissertations / Theses on the topic "Gibbs-type priors"

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CORRADIN, RICCARDO. "Contributions to modelling via Bayesian nonparametric mixtures." Doctoral thesis, Università degli Studi di Milano-Bicocca, 2019. http://hdl.handle.net/10281/241261.

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I modelli mistura in ambito Bayesiano nonparametrico sono modelli flessibili per stime di densità e clustering, ormai uno strumento di uso comune in ambito statistico applicato. Il primo modello introdotto in questo ambito è stato il processo di Dirichlet (DP) (Ferguson, 1973) combinato con un kernel Gaussiano(Lo, 1984). Recentemente è cresciuto l’interesse verso la definizione di modelli mistura basati su misure nonparametriche che generalizzano il DP. Tra le misure proposte, il processo di Pitman-Yor (PY) (Perman et al., 1992; Pitman, 1995) e, più in generale, la classe di Gibbs-type prior (
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Book chapters on the topic "Gibbs-type priors"

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Tamminen, Toni, and Jouko Lampinen. "Bayesian Object Matching with Hierarchical Priors and Markov Chain Monte Carlo." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0049.

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Abstract We present an object matching system where Bayesian inference is used for estimating the probability distribution of object feature locations in a digital image. The representation of the object contains two parts: the likelihood part which defines the probability of perceiving a pixel image corresponding to the object detail, and a hierarchical prior part which defines the distributions of the feature locations relative to the other features. In this study the objects are human faces. The likelihood part is based on Gabor filters, which are a type of optimal bandpass digital filters.
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Journal articles
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