Relevant bibliographies by topics / Bayesian non-Parametric model

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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 'Bayesian non-Parametric model'

Author: Grafiati
Published: 25 May 2024
Last updated: 19 July 2025

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Journal articles on the topic "Bayesian non-Parametric model"

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Assaf, A. George, Mike Tsionas, Florian Kock, and Alexander Josiassen. "A Bayesian non-parametric stochastic frontier model." Annals of Tourism Research 87 (March 2021): 103116. http://dx.doi.org/10.1016/j.annals.2020.103116.

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Assaf, A. George, Mike Tsionas, Florian Kock, and Alexander Josiassen. "A Bayesian non-parametric stochastic frontier model." Annals of Tourism Research 87 (March 2021): 103116. http://dx.doi.org/10.1016/j.annals.2020.103116.

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LI, R., J. ZHOU, and L. WANG. "ESTIMATION OF THE BINARY LOGISTIC REGRESSION MODEL PARAMETER USING BOOTSTRAP RE-SAMPLING." Latin American Applied Research - An international journal 48, no. 3 (2018): 199–204. http://dx.doi.org/10.52292/j.laar.2018.228.

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In this paper, the non-parametric bootstrap and non-parametric Bayesian bootstrap methods are applied for parameter estimation in the binary logistic regression model. A real data study and a simulation study are conducted to compare the Nonparametric bootstrap, Non-parametric Bayesian bootstrap and the maximum likelihood methods. Study results shows that three methods are all effective ways for parameter estimation in the binary logistic regression model. In small sample case, the non-parametric Bayesian bootstrap method performs relatively better than the non-parametric bootstrap and the max
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Alamri, Faten S., Edward L. Boone, and David J. Edwards. "A Bayesian Monotonic Non-parametric Dose-Response Model." Human and Ecological Risk Assessment: An International Journal 27, no. 8 (2021): 2104–23. http://dx.doi.org/10.1080/10807039.2021.1956298.

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Minh Nguyen, Thanh, and Q. M. Jonathan Wu. "A non-parametric Bayesian model for bounded data." Pattern Recognition 48, no. 6 (2015): 2084–95. http://dx.doi.org/10.1016/j.patcog.2014.12.019.

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Xia, Yunqing. "Application of non parametric Bayesian methods in high dimensional data." Journal of Computational Methods in Sciences and Engineering 24, no. 2 (2024): 731–43. http://dx.doi.org/10.3233/jcm-237104.

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With the development of technology and the widespread collection of data, high-dimensional data analysis has become a research hotspot in many fields. Traditional parameter methods often face problems such as dimensional disasters in high-dimensional data analysis. Non parametric methods have broad application prospects in high-dimensional data because they do not rely on specific parameter distribution assumptions. The Bayesian rule is more suitable for dealing with noise and outliers in high-dimensional data because it takes uncertainty into account. Therefore, it is of great significance to
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Dong, Alice X. D., Jennifer S. K. Chan, and Gareth W. Peters. "RISK MARGIN QUANTILE FUNCTION VIA PARAMETRIC AND NON-PARAMETRIC BAYESIAN APPROACHES." ASTIN Bulletin 45, no. 3 (2015): 503–50. http://dx.doi.org/10.1017/asb.2015.8.

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AbstractWe develop quantile functions from regression models in order to derive risk margin and to evaluate capital in non-life insurance applications. By utilizing the entire range of conditional quantile functions, especially higher quantile levels, we detail how quantile regression is capable of providing an accurate estimation of risk margin and an overview of implied capital based on the historical volatility of a general insurers loss portfolio. Two modeling frameworks are considered based around parametric and non-parametric regression models which we develop specifically in this insura
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Li, Hong, and Yang Lu. "A Bayesian non-parametric model for small population mortality." Scandinavian Actuarial Journal 2018, no. 7 (2018): 605–28. http://dx.doi.org/10.1080/03461238.2017.1418420.

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Abdulsamad Habeeb *, Ahmed, and Qutaiba N. Nayef Al-Kazaz. "A comparison between Speckman and Bayesian estimation method of a semiparametric balanced longitudinal data model." Journal of Economics and Administrative Sciences 30, no. 142 (2024): 449–64. http://dx.doi.org/10.33095/kpscqv37.

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This paper aims to use semi-parametric regression to balanced longitudinal data model, where the parametric regression models suffer from the problem of strict constraints, while non-parametric regression models, despite their flexibility, suffer from the problem of the curse of dimensionality. Consequently, semi-parametric regression is an ideal solution to get rid of the problems that parametric and non-parametric regression suffer from. The great advantage of this model is that it contains all the positive features included in the previous two models, such as containing strict restrictions
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MILADINOVIC, BRANKO, and CHRIS P. TSOKOS. "SENSITIVITY OF THE BAYESIAN RELIABILITY ESTIMATES FOR THE MODIFIED GUMBEL FAILURE MODEL." International Journal of Reliability, Quality and Safety Engineering 16, no. 04 (2009): 331–41. http://dx.doi.org/10.1142/s0218539309003423.

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The classical Gumbel probability distribution is modified in order to study the failure times of a given system. Bayesian estimates of the reliability function under five different parametric priors and the square error loss are studied. The Bayesian reliability estimate under the non-parametric kernel density prior is compared with those under the parametric priors and numerical computations are given to study their effectiveness.
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Dissertations / Theses on the topic "Bayesian non-Parametric model"

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Bartcus, Marius. "Bayesian non-parametric parsimonious mixtures for model-based clustering." Thesis, Toulon, 2015. http://www.theses.fr/2015TOUL0010/document.

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Cette thèse porte sur l’apprentissage statistique et l’analyse de données multi-dimensionnelles. Elle se focalise particulièrement sur l’apprentissage non supervisé de modèles génératifs pour la classification automatique. Nous étudions les modèles de mélanges Gaussians, aussi bien dans le contexte d’estimation par maximum de vraisemblance via l’algorithme EM, que dans le contexte Bayésien d’estimation par Maximum A Posteriori via des techniques d’échantillonnage par Monte Carlo. Nous considérons principalement les modèles de mélange parcimonieux qui reposent sur une décomposition spectrale de
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Ren, Yan. "A Non-parametric Bayesian Method for Hierarchical Clustering of Longitudinal Data." University of Cincinnati / OhioLINK, 2012. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1337085531.

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Gebremeskel, Haftu Gebrehiwot. "Implementing hierarchical bayesian model to fertility data: the case of Ethiopia." Doctoral thesis, Università degli studi di Padova, 2016. http://hdl.handle.net/11577/3424458.

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Background: Ethiopia is a country with 9 ethnically-based administrative regions and 2 city administrations, often cited, among other things, with high fertility rates and rapid population growth rate. Despite the country’s effort in their reduction, they still remain high, especially at regional-level. To this end, the study of fertility in Ethiopia, particularly on its regions, where fertility variation and its repercussion are at boiling point, is paramount important. An easy way of finding different characteristics of a fertility distribution is to build a suitable model of fertility patte
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Bratières, Sébastien. "Non-parametric Bayesian models for structured output prediction." Thesis, University of Cambridge, 2018. https://www.repository.cam.ac.uk/handle/1810/274973.

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Structured output prediction is a machine learning tasks in which an input object is not just assigned a single class, as in classification, but multiple, interdependent labels. This means that the presence or value of a given label affects the other labels, for instance in text labelling problems, where output labels are applied to each word, and their interdependencies must be modelled. Non-parametric Bayesian (NPB) techniques are probabilistic modelling techniques which have the interesting property of allowing model capacity to grow, in a controllable way, with data complexity, while maint
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Zhang, Jufen. "Bayesian density estimation and classification of incomplete data using semi-parametric and non parametric models." Thesis, University of Exeter, 2006. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.426082.

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Xu, Yangyi. "Frequentist-Bayesian Hybrid Tests in Semi-parametric and Non-parametric Models with Low/High-Dimensional Covariate." Diss., Virginia Tech, 2014. http://hdl.handle.net/10919/71285.

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We provide a Frequentist-Bayesian hybrid test statistic in this dissertation for two testing problems. The first one is to design a test for the significant differences between non-parametric functions and the second one is to design a test allowing any departure of predictors of high dimensional X from constant. The implementation is also given in construction of the proposal test statistics for both problems. For the first testing problem, we consider the statistical difference among massive outcomes or signals to be of interest in many diverse fields including neurophysiology, imaging, e
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Knowles, David Arthur. "Bayesian non-parametric models and inference for sparse and hierarchical latent structure." Thesis, University of Cambridge, 2012. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.610403.

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Hadrich, Ben Arab Atizez. "Étude des fonctions B-splines pour la fusion d'images segmentées par approche bayésienne." Thesis, Littoral, 2015. http://www.theses.fr/2015DUNK0385/document.

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Dans cette thèse nous avons traité le problème de l'estimation non paramétrique des lois de probabilités. Dans un premier temps, nous avons supposé que la densité inconnue f a été approchée par un mélange de base B-spline quadratique. Puis, nous avons proposé un nouvel estimateur de la densité inconnue f basé sur les fonctions B-splines quadratiques, avec deux méthodes d'estimation. La première est base sur la méthode du maximum de vraisemblance et la deuxième est basée sur la méthode d'estimation Bayésienne MAP. Ensuite, nous avons généralisé notre étude d'estimation dans le cadre du mélange
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Yang, Sikun [Verfasser], Heinz Akademischer Betreuer] Köppl, and Kristian [Akademischer Betreuer] [Kersting. "Non-parametric Bayesian Latent Factor Models for Network Reconstruction / Sikun Yang ; Heinz Köppl, Kristian Kersting." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://nbn-resolving.de/urn:nbn:de:tuda-tuprints-96957.

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Yang, Sikun [Verfasser], Heinz [Akademischer Betreuer] Köppl, and Kristian [Akademischer Betreuer] Kersting. "Non-parametric Bayesian Latent Factor Models for Network Reconstruction / Sikun Yang ; Heinz Köppl, Kristian Kersting." Darmstadt : Universitäts- und Landesbibliothek Darmstadt, 2020. http://d-nb.info/1204200769/34.

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Books on the topic "Bayesian non-Parametric model"

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Florens, J. P., M. Mouchart, J. P. Raoult, L. Simar, and A. F. M. Smith. Specifying Statistical Models: From Parametric to Non-Parametric, Using Bayesian or Non-Bayesian Approaches. Springer London, Limited, 2012.

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Brazier, John, Julie Ratcliffe, Joshua A. Salomon, and Aki Tsuchiya. Modelling health state valuation data. Oxford University Press, 2016. http://dx.doi.org/10.1093/med/9780198725923.003.0005.

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This chapter examines the technical issues in modelling health state valuation data. Most measures of health define too many states to directly value all of them (e.g. SF-6D defines 18,000 health states). The solution has been to value a subset and by using modelling to predict the values of all states. This chapter reviews two approaches to modelling: one using multiattribute utility theory to determine health values given an assumed functional form; and the other is using statistical modelling of SF-6D preference data that are skewed, bimodal, and clustered by respondents. This chapter exami
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Book chapters on the topic "Bayesian non-Parametric model"

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Khoufache, Reda, Anisse Belhadj, Hanene Azzag, and Mustapha Lebbah. "Distributed MCMC Inference for Bayesian Non-parametric Latent Block Model." In Advances in Knowledge Discovery and Data Mining. Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2242-6_22.

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Lanteri, Alessandro, Raffaele Argiento, and Silvia Montagna. "A Bayesian Non-Parametric Model to Learn Functions with Discontinuties." In Contributions to Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-92383-8_37.

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Wang, Tairan, Sifeng Bi, and Jianfeng Huang. "Parametric and Non-parametric Stochastic Damage Detection Based on Bayesian Model Updating Framework with Hybrid Uncertainties." In Proceedings of the UNIfied Conference of DAMAS, IncoME and TEPEN Conferences (UNIfied 2023). Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-49421-5_36.

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Batmanghelich, Nematollah Kayhan, Ardavan Saeedi, Raul San Jose Estepar, Michael Cho, and William M. Wells. "Inferring Disease Status by Non-parametric Probabilistic Embedding." In Medical Computer Vision and Bayesian and Graphical Models for Biomedical Imaging. Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-61188-4_5.

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Alhaji, Baba B., Hongsheng Dai, Yoshiko Hayashi, Veronica Vinciotti, Andrew Harrison, and Berthold Lausen. "Analysis of ChIP-seq Data Via Bayesian Finite Mixture Models with a Non-parametric Component." In Analysis of Large and Complex Data. Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-25226-1_43.

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Almeida, Carlos, and Michel Mouchart. "Bayesian Encompassing Specification Test Under Not Completely Known Partial Observability*." In Bayesian Statistics 8. Oxford University PressOxford, 2007. http://dx.doi.org/10.1093/oso/9780199214655.003.0021.

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Abstract A Bayesian specification test based on the encompassing principle for the case of partial observability is proposed. A structural parametric null model is compared against a nonparametric alternative model at the level of latent variables. A same observability process is introduced in both models. The comparison is made between the posterior measures of the non-Euclidean parameter (of the alternative model) in the extended and in the alternative models. The general development is illustrated with an example where a linear combination of a latent vector is only observed.
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Walker, S. G., and J. C. Wakefield. "Bayesian Approaches to the Population Modelling of a Monotonic Dose-Response Relation." In Bayesian Statistics 5. Oxford University PressOxford, 1996. http://dx.doi.org/10.1093/oso/9780198523567.003.0059.

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Abstract In a dose-ranging study the goal is to establish an initial dose of a new drug for patients from the relevant target population. This may be achieved by observing individual responses over a range of dose levels from a randomly selected sample from the patient population. When data from such studies is analysed it is important to acknowledge between patient variability. Typically this is done using a hierarchical model with a (parametric) non-linear first stage model. Here we assume that a parametric dose-response curve is unknown but that a monotonic dose-response relation exists. We propose two semiparametric forms and demonstrate their use with simulated data.
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"Mobile Robot Localization with Recursive Bayesian Filters." In Simultaneous Localization and Mapping for Mobile Robots. IGI Global, 2013. http://dx.doi.org/10.4018/978-1-4666-2104-6.ch007.

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In this last chapter of the second section, the authors present probabilistic solutions to mobile robot localization that bring together the recursive filters introduced in chapter 4 and all the components and models already discussed in the preceding chapters. It presents the general, Bayesian framework for a probabilistic solution to localization and mapping. The problem is formally described as a graphical model (in particular a dynamic Bayesian network), and the characteristics that can be exploited to approach it efficiently are elaborated. Among parametric Bayesian estimators, the family of the Kalman filters is introduced with examples and practical applications. Then, the more modern non-parametric filters, mainly particle filters, are explained. Due to the diversity of filters available for localization, comparative tables are included.
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Bandyopadhyay, Arindam. "Statistical Tools for Model Validation and Back Testing." In Basic Statistics for Risk Management in Banks and Financial Institutions. Oxford University Press, 2022. http://dx.doi.org/10.1093/oso/9780192849014.003.0009.

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Model validation and calibration chapter demonstrate key statistical tests that are useful to measure predictive power of risk models. It mainly assesses the critical steps, data input quality, and discriminatory power of the models in predicting default or loss. Model validation has been a key task for risk-focused management for internal management of risk across various business lines. Reliable rating systems require efficient validation strategies. This chapter explains power curve-fitting techniques to assess discriminatory power of predictive models, method for checking model errors, and estimation of model accuracy in great detail. The separation power check through information value and KS test and their utility in scorecard development has been elaborated. Steps in Hosmer–Lemeshow goodness-of-fit test pertaining to logistic model and other non-parametric validation checks like Akaike information criteria, Bayesian information criterion, Kendal’s tau are described in this chapter. An independent and objective validation of the predictive power and efficacy of valuation and risk models through statistical tests is an integral part of a robust risk management system.
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Han Xian-Hua, Chen Yen-Wei, and Xu Gang. "Bayesian-based Saliency Model for Liver Tumor Enhancement." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2014. https://doi.org/10.3233/978-1-61499-405-3-357.

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Automatic tumor enhancement and detection has an essential role for the computer-aided diagnosis of liver tumor in CT volume data. This paper proposes a novel tumor enhancement strategy by extracting a tumor saliency map, which represents the uncommon or tumor tissue compared to the liver and vessel ones in CT volumes. The saliency map can be constructed by exploring the existing probability of tumor in any voxel. However, the tumor prototypes in a test liver volume from a specific patient or common tumor prototypes are extremely difficult to achieve due to requirement of full-searching and large variation of tumor tissues in different liver volumes. Therefore, this paper investigates a tumor-training-data free strategy by only constructing the common healthy liver and vessel prototypes, which can be extracted from any slice of a liver volume, and then applies a nonparametric Bayesian framework for calculating the existing probability of liver or vessel. Finally, the existing probability of tumor can be deduced from that of liver or vessel. The advantages of our proposed strategy mainly include three aspects: (1) it only needs to construct the prototypes of common tissue such as liver or vessel region, which are easily obtained in any liver volume; (2) it proposes an adaptive non-parametric framework for tumor enhancement, which does not need to learn a common classification model for all liver volumes; (3) dispensable to remove the other different structure such as vessel in liver volume as a pre-processing step. Experiments validate that the proposed Bayesian-based saliency model for liver tumor enhancement can perform much better than the conventional approaches such as EM, EM/MPM tumor segmentation methods.
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Conference papers on the topic "Bayesian non-Parametric model"

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Matteoli, Stefania, Marco Diani, and Giovanni Corsini. "Bayesian Non-Parametric Detector Based on the Replacement Model." In IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium. IEEE, 2022. http://dx.doi.org/10.1109/igarss46834.2022.9883554.

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Zhuang, Peixian, Wei Wang, Delu Zeng, and Xinghao Ding. "Robust mixed noise removal with non-parametric Bayesian sparse outlier model." In 2014 IEEE 16th International Workshop on Multimedia Signal Processing (MMSP). IEEE, 2014. http://dx.doi.org/10.1109/mmsp.2014.6958792.

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Ensafi, Shahab, Shijian Lu, Ashraf A. Kassim, and Chew Lim Tan. "Sparse non-parametric Bayesian model for HEP-2 cell image classification." In 2015 IEEE 12th International Symposium on Biomedical Imaging (ISBI 2015). IEEE, 2015. http://dx.doi.org/10.1109/isbi.2015.7163964.

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Fujimoto, Masakiyo, Yotaro Kubo, and Tomohiro Nakatani. "Unsupervised non-parametric Bayesian modeling of non-stationary noise for model-based noise suppression." In ICASSP 2014 - 2014 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2014. http://dx.doi.org/10.1109/icassp.2014.6854667.

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Otoshi, Tatsuya, Shin'ichi Arakawa, Masayuki Murata, and Takeo Hosomi. "Non-parametric Decision-Making by Bayesian Attractor Model for Dynamic Slice Selection." In GLOBECOM 2021 - 2021 IEEE Global Communications Conference. IEEE, 2021. http://dx.doi.org/10.1109/globecom46510.2021.9685972.

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Aliamiri, A., J. Stalnaker, and E. Miller. "A Bayesian Approach for Classification of Buried Objects using Non-Parametric Prior Model." In 2006 IEEE International Symposium on Geoscience and Remote Sensing. IEEE, 2006. http://dx.doi.org/10.1109/igarss.2006.1003.

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"NON-PARAMETRIC BAYESIAN ALIGNMENT AND RECOVERY OF OCCLUDED FACE USING DIRECT COMBINED MODEL." In International Conference on Computer Vision Theory and Applications. SciTePress - Science and and Technology Publications, 2010. http://dx.doi.org/10.5220/0002833704950498.

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Zhou, Deyu, Xuan Zhang, and Yulan He. "Event extraction from Twitter using Non-Parametric Bayesian Mixture Model with Word Embeddings." In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers. Association for Computational Linguistics, 2017. http://dx.doi.org/10.18653/v1/e17-1076.

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Kamigaito, Hidetaka, Taro Watanabe, Hiroya Takamura, Manabu Okumura, and Eiichiro Sumita. "Hierarchical Back-off Modeling of Hiero Grammar based on Non-parametric Bayesian Model." In Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 2015. http://dx.doi.org/10.18653/v1/d15-1143.

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Giremus, Audrey, and Vincent Pereira. "A Bayesian non parametric time-switching autoregressive model for multipath errors in GPS navigation." In 2016 IEEE Sensor Array and Multichannel Signal Processing Workshop (SAM). IEEE, 2016. http://dx.doi.org/10.1109/sam.2016.7569698.

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Reports on the topic "Bayesian non-Parametric model"

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Petrova, Katerina. On the Validity of Classical and Bayesian DSGE-Based Inference. Federal Reserve Bank of New York, 2024. http://dx.doi.org/10.59576/sr.1084.

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This paper studies large sample classical and Bayesian inference in a prototypical linear DSGE model and demonstrates that inference on the structural parameters based on a Gaussian likelihood is unaffected by departures from Gaussianity of the structural shocks. This surprising result is due to a cancellation in the asymptotic variance resulting into a generalized information equality for the block corresponding to the structural parameters. The underlying reason for the cancellation is the certainty equivalence property of the linear rational expectation model. The main implication of this r
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