Дисертації з теми "Hierarchical Bayesian Priors"
Оформте джерело за APA, MLA, Chicago, Harvard та іншими стилями
Ознайомтеся з топ-19 дисертацій для дослідження на тему "Hierarchical Bayesian Priors".
Біля кожної праці в переліку літератури доступна кнопка «Додати до бібліографії». Скористайтеся нею – і ми автоматично оформимо бібліографічне посилання на обрану працю в потрібному вам стилі цитування: APA, MLA, «Гарвард», «Чикаго», «Ванкувер» тощо.
Також ви можете завантажити повний текст наукової публікації у форматі «.pdf» та прочитати онлайн анотацію до роботи, якщо відповідні параметри наявні в метаданих.
Переглядайте дисертації для різних дисциплін та оформлюйте правильно вашу бібліографію.
Israeli, Yeshayahu D. "Whitney Element Based Priors for Hierarchical Bayesian Models." Case Western Reserve University School of Graduate Studies / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=case1621866603265673.
Повний текст джерелаGeorge, Robert Emerson. "The role of hierarchical priors in robust Bayesian inference /." The Ohio State University, 1993. http://rave.ohiolink.edu/etdc/view?acc_num=osu1487847761308082.
Повний текст джерелаSonksen, Michael David. "Bayesian Model Diagnostics and Reference Priors for Constrained Rate Models of Count Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1312909127.
Повний текст джерелаPfarrhofer, Michael, and Philipp Piribauer. "Flexible shrinkage in high-dimensional Bayesian spatial autoregressive models." Elsevier, 2019. http://epub.wu.ac.at/6839/1/1805.10822.pdf.
Повний текст джерелаBitto, Angela, and Sylvia Frühwirth-Schnatter. "Achieving shrinkage in a time-varying parameter model framework." Elsevier, 2019. http://dx.doi.org/10.1016/j.jeconom.2018.11.006.
Повний текст джерелаFeldkircher, Martin, Florian Huber, and Gregor Kastner. "Sophisticated and small versus simple and sizeable: When does it pay off to introduce drifting coefficients in Bayesian VARs?" WU Vienna University of Economics and Business, 2018. http://epub.wu.ac.at/6021/1/wp260.pdf.
Повний текст джерелаSeries: Department of Economics Working Paper Series
Pirathiban, Ramethaa. "Improving species distribution modelling: Selecting absences and eliciting variable usefulness for input into standard algorithms or a Bayesian hierarchical meta-factor model." Thesis, Queensland University of Technology, 2019. https://eprints.qut.edu.au/134401/1/Ramethaa_Pirathiban_Thesis.pdf.
Повний текст джерелаManandhar, Binod. "Bayesian Models for the Analyzes of Noisy Responses From Small Areas: An Application to Poverty Estimation." Digital WPI, 2017. https://digitalcommons.wpi.edu/etd-dissertations/188.
Повний текст джерелаEgidi, Leonardo. "Developments in Bayesian Hierarchical Models and Prior Specification with Application to Analysis of Soccer Data." Doctoral thesis, Università degli studi di Padova, 2018. http://hdl.handle.net/11577/3427270.
Повний текст джерелаNegli ultimi anni la sfida per la specificazione di nuove distribuzioni a priori e per l’uso di complessi modelli gerarchici è diventata ancora più rilevante all’interno dell’inferenza Bayesiana. L’avvento delle tecniche Markov Chain Monte Carlo, insieme a nuovi linguaggi di programmazione probabilistici, ha esteso i confini del campo, sia in direzione teorica che applicata. Nella presente tesi ci dedichiamo a obiettivi teorici e applicati. Nella prima parte proponiamo una nuova classe di distribuzioni a priori che dipendono dai dati e che sono specificate tramite una mistura tra una a priori non informativa e una a priori informativa. La generica distribuzione appartenente a questa nuova classe fornisce meno informazione di una priori informativa e si candida a non dominare le conclusioni inferenziali quando la dimensione campionaria è piccola o moderata. Tale distribuzione `e idonea per scopi di robustezza, specialmente in caso di scorretta specificazione della distribuzione a priori informativa. Alcuni studi di simulazione all’interno di modelli coniugati mostrano che questa proposta può essere conveniente per ridurre gli errori quadratici medi e per migliorare la copertura frequentista. Inoltre, sotto condizioni non restrittive, questa classe di distribuzioni d`a luogo ad alcune altre interessanti proprietà teoriche. Nella seconda parte della tesi usiamo la classe dei modelli gerarchici Bayesiani per prevedere alcune grandezze relative al gioco del calcio ed estendiamo l’usuale modellazione per i goal includendo nel modello un’ulteriore informazione proveniente dalle case di scommesse. Strumenti per sondare a posteriori la bontà di adattamento del modello ai dati mettono in luce un’ottima aderenza del modello ai dati in possesso, una buona calibrazione dello stesso e suggeriscono, infine, la costruzione di efficienti strategie di scommesse per dati futuri.
Frühwirth-Schnatter, Sylvia, and Regina Tüchler. "Bayesian parsimonious covariance estimation for hierarchical linear mixed models." Institut für Statistik und Mathematik, WU Vienna University of Economics and Business, 2004. http://epub.wu.ac.at/774/1/document.pdf.
Повний текст джерелаSeries: Research Report Series / Department of Statistics and Mathematics
Feldkircher, Martin, and Florian Huber. "Adaptive Shrinkage in Bayesian Vector Autoregressive Models." WU Vienna University of Economics and Business, 2016. http://epub.wu.ac.at/4933/1/wp221.pdf.
Повний текст джерелаSeries: Department of Economics Working Paper Series
Schaper, Andrew. "Informative Prior Distributions in Multilevel/Hierarchical Linear Growth Models: Demonstrating the Use of Bayesian Updating for Fixed Effects." Thesis, University of Oregon, 2014. http://hdl.handle.net/1794/18366.
Повний текст джерелаBord, Séverine. "Estimation Bayésienne de l’abondance par "removal sampling" en présence de variabilité du taux d’échantillonnage : application aux tiques Ixodes ricinus en quête d’hôtes." Thesis, Clermont-Ferrand 2, 2014. http://www.theses.fr/2014CLF22463/document.
Повний текст джерелаThe estimation of animal abundance is essential to understand population dynamics, species interactions and disease patterns in populations and to estimate the risk of pathogens transmission. Several sampling methods such as single counts, distance sampling, removal sampling or capture mark recapture could be used to estimate abundance. In this study, we are investigated the abundance of Ixodes ricinus ticks, which are involved in the transmission of many pathogens. Tick abundance is commonly estimated by the number of nymphs captured during a single observation (a cloth dragged on a given surface). In this case, analyses of abundance patterns assumes that the probability of detecting a tick, hence the sampling rate, remains constant across the observations. In practice, however, this assumption is often not satisfied as the sampling rate may fluctuate between observation plots. The variation of sampling rate is never taken into account in estimations of tick abundance. Using a removal sampling design (RS), (i) we showed that the sampling rate and the usual abundance indicator (based on a single drag observation per spot) were both influenced by environmental conditions ; (ii) we proposed a method to determine the abundance indicator the least influenced by sampling rate variations ; (iii) using a hierarchical Bayesian model, we estimated simultaneously the abundance and the sampling rate according the type of vegetation, and the time of sampling. The sampling rate varied between 33,9 % and 47,4 % for shrubs and 53,6 % and 66,7 % for dead leaves. In addition, we show that the RS model tends to Poisson iid model when the population size N0 tends to infinite. This result conduct to infinite estimations for N0. We show that (i) Bayesian estimators were divergent for vague prior ; (ii) β(a, b) prior for a > 2 on τ conduct to convergent estimators. Then, we proposed recommendations for prior choice for τ parameter to give good estimations of N0 or τ. We discuss the relevance of RS for ticks and the possible perspectives to (i) estimate the acarologic risk associated to all potential active ticks for given spot, (ii) estimate the risk at the larger scale, i.e. how to distribute the sampling effort between number of spot and number of consecutive sampling by spot
Papoutsis, Panayotis. "Potentiel et prévision des temps d'attente pour le covoiturage sur un territoire." Thesis, Ecole centrale de Nantes, 2021. http://www.theses.fr/2021ECDN0059.
Повний текст джерелаThis thesis focuses on the potential and prediction of carpooling waiting times in a territory using statistical learning methods. Five main themes are covered in this manuscript. The first presents quantile regression techniques to predict waiting times. The second details the construction of a workflow based on Geographic Information Systems (GIS) tools in order to fully leverage the carpooling data. In a third part we develop a hierarchical bayesian model in order to predict traffic flows and waiting times. In the fourth part, we propose a methodology for constructing an informative prior by bayesian transfer to improve the prediction of waiting times for a short dataset situation. Lastly, the final theme focuses on the production and industrial exploitation of the bayesian hierarchical model
Chen, Zhu 1985. "The effects of three different priors for variance parameters in the normal-mean hierarchical model." Thesis, 2010. http://hdl.handle.net/2152/ETD-UT-2010-05-1516.
Повний текст джерелаtext
Hossain, Md Shahadut. "A conservative prior for bayesian hierarchical models in biostatistics." Thesis, 2003. http://hdl.handle.net/2429/14419.
Повний текст джерелаLi, Yingbo. "Bayesian Hierarchical Models for Model Choice." Diss., 2013. http://hdl.handle.net/10161/8063.
Повний текст джерелаWith the development of modern data collection approaches, researchers may collect hundreds to millions of variables, yet may not need to utilize all explanatory variables available in predictive models. Hence, choosing models that consist of a subset of variables often becomes a crucial step. In linear regression, variable selection not only reduces model complexity, but also prevents over-fitting. From a Bayesian perspective, prior specification of model parameters plays an important role in model selection as well as parameter estimation, and often prevents over-fitting through shrinkage and model averaging.
We develop two novel hierarchical priors for selection and model averaging, for Generalized Linear Models (GLMs) and normal linear regression, respectively. They can be considered as "spike-and-slab" prior distributions or more appropriately "spike- and-bell" distributions. Under these priors we achieve dimension reduction, since their point masses at zero allow predictors to be excluded with positive posterior probability. In addition, these hierarchical priors have heavy tails to provide robust- ness when MLE's are far from zero.
Zellner's g-prior is widely used in linear models. It preserves correlation structure among predictors in its prior covariance, and yields closed-form marginal likelihoods which leads to huge computational savings by avoiding sampling in the parameter space. Mixtures of g-priors avoid fixing g in advance, and can resolve consistency problems that arise with fixed g. For GLMs, we show that the mixture of g-priors using a Compound Confluent Hypergeometric distribution unifies existing choices in the literature and maintains their good properties such as tractable (approximate) marginal likelihoods and asymptotic consistency for model selection and parameter estimation under specific values of the hyper parameters.
While the g-prior is invariant under rotation within a model, a potential problem with the g-prior is that it inherits the instability of ordinary least squares (OLS) estimates when predictors are highly correlated. We build a hierarchical prior based on scale mixtures of independent normals, which incorporates invariance under rotations within models like ridge regression and the g-prior, but has heavy tails like the Zeller-Siow Cauchy prior. We find this method out-performs the gold standard mixture of g-priors and other methods in the case of highly correlated predictors in Gaussian linear models. We incorporate a non-parametric structure, the Dirichlet Process (DP) as a hyper prior, to allow more flexibility and adaptivity to the data.
Dissertation
Pandey, Gaurav. "Deep Learning with Minimal Supervision." Thesis, 2017. http://etd.iisc.ac.in/handle/2005/4315.
Повний текст джерелаXu, Lizhen. "Bayesian Methods for Genetic Association Studies." Thesis, 2012. http://hdl.handle.net/1807/34972.
Повний текст джерела