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Relevant bibliographies by topics / Non-informative priors

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Academic literature on the topic 'Non-informative priors'

Author: Grafiati

Published: 4 June 2021

Last updated: 25 July 2025

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Journal articles on the topic "Non-informative priors"

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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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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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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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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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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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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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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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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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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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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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Dissertations / Theses on the topic "Non-informative priors"

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Sonksen, 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.

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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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Submitted by (ana.araujo@ufrpe.br) on 2016-05-25T16:16:39Z No. of bitstreams: 1 Cicero Carlos Felix de Oliveira.pdf: 934310 bytes, checksum: 4f4332b0b319f6bf33cdc1d615c36324 (MD5)<br>Made available in DSpace on 2016-05-25T16:16:39Z (GMT). No. of bitstreams: 1 Cicero Carlos Felix de Oliveira.pdf: 934310 bytes, checksum: 4f4332b0b319f6bf33cdc1d615c36324 (MD5) Previous issue date: 2011-07-08<br>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 di

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Kamary, Kaniav. "Lois a priori non-informatives et la modélisation par mélange." Thesis, Paris Sciences et Lettres (ComUE), 2016. http://www.theses.fr/2016PSLED022/document.

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L’une des grandes applications de la statistique est la validation et la comparaison de modèles probabilistes au vu des données. Cette branche des statistiques a été développée depuis la formalisation de la fin du 19ième siècle par des pionniers comme Gosset, Pearson et Fisher. Dans le cas particulier de l’approche bayésienne, la solution à la comparaison de modèles est le facteur de Bayes, rapport des vraisemblances marginales, quelque soit le modèle évalué. Cette solution est obtenue par un raisonnement mathématique fondé sur une fonction de coût.Ce facteur de Bayes pose cependant problème e

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Svennblad, Bodil. "On Estimating Topology and Divergence Times in Phylogenetics." Doctoral thesis, Uppsala University, Mathematical Statistics, 2008. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-8441.

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<p>This PhD thesis consists of an introduction and five papers, dealing with statistical methods in phylogenetics.</p><p>A phylogenetic tree describes the evolutionary relationships among species assuming that they share a common ancestor and that evolution takes place in a tree like manner. Our aim is to reconstruct the evolutionary relationships from aligned DNA sequences.</p><p>In the first two papers we investigate two measures of confidence for likelihood based methods, bootstrap frequencies with Maximum Likelihood (ML) and Bayesian posterior probabilities. We show that an earlier claimed

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Min, Namhong. "A method to establish non-informative prior probabilities for risk-based decision analysis." Thesis, 2008. http://hdl.handle.net/2152/24330.

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In Bayesian decision analysis, uncertainty and risk are accounted for with probabilities for the possible states, or states of nature, that affect the outcome of a decision. Application of Bayes’ theorem requires non-informative prior probabilities, which represent the probabilities of states of nature for a decision maker under complete ignorance. These prior probabilities are then subsequently updated with any and all available information in assessing probabilities for making decisions. The conventional approach for the non-informative probability distribution is based on Bernoulli’s princi

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Books on the topic "Non-informative priors"

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Tibshirani, Robert. Non-informative priors for one parameter of many. University of Toronto, Dept. of Statistics, 1987.

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Koenig, Christoph, Sarah Depaoli, Haiyan Liu, and Rens Van De Schoot, eds. Moving Beyond Non-Informative Prior Distributions: Achieving the Full Potential of Bayesian Methods for Psychological Research. Frontiers Media SA, 2022. http://dx.doi.org/10.3389/978-2-88974-214-1.

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Walden, Michael. Smart Economics. Greenwood Publishing Group, Inc., 2005. http://dx.doi.org/10.5040/9798216015444.

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Budget deficits, gas prices, health care costs, social security, job security…. Anxiety over the economy pervades our daily lives—from reports on the early morning newscasts to gossip around the water cooler to dinner table debate. Yet most citizens are woefully ignorant when it comes to understanding how the economy works and how to interpret the impact of policies and business decisions. It's easy to slip into generalities: government spending is wasteful, taxes are too high, good-paying jobs are being shipped overseas, Americans don't save enough. Other issues become hijacked by political p

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Book chapters on the topic "Non-informative priors"

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Ascolani, Filippo, Ylenia F. Buttigliero, and Matteo Ruggiero. "Non-Informative Priors in Wright–Fisher Smoothing." In Italian Statistical Society Series on Advances in Statistics. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-64431-3_16.

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Ghosal, S., J. K. Ghosh, and R. V. Ramamoorthi. "Non-Informative Priors Via Sieves and Packing Numbers." In Advances in Statistical Decision Theory and Applications. Birkhäuser Boston, 1997. http://dx.doi.org/10.1007/978-1-4612-2308-5_8.

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Di Maso, Matteo, Monica Ferraroni, Pasquale Ferrante, Serena Delbue, and Federico Ambrogi. "Longitudinal profile of a set of biomarkers in predicting Covid-19 mortality using joint models." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.36.

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In survival analysis, time-varying covariates are endogenous when their measurements are directly related to the event status and incomplete information occur at random points during the follow-up. Consequently, the time-dependent Cox model leads to biased estimates. Joint models (JM) allow to correctly estimate these associations combining a survival and longitudinal sub-models by means of a shared parameter (i.e., random effects of the longitudinal sub-model are inserted in the survival one). This study aims at showing the use of JM to evaluate the association between a set of inflammatory b

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Ghosh, Jayanta K., and Rahul Mukerjee. "Non-informative Priors." In Bayesian Statistics 4. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198522669.003.0011.

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Abstract This paper reviews non-informative priors based on considerations of (i) entropy, (ii) matching what a frequentist might do and (iii) weak minimaxity. Some new results have also been presented. The best way to think of a non-informative prior is, as Bernardo (1979) has argued, as a sort of origin or reference point against which any given p1ior reflecting some subjective opinion can be judged. In fact, it would be quite appropriate to call all candidates for non-informative priors reference priors. However, in what follows, the term reference prior will be used only for the p1iors pro

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Donovan, Therese M., and Ruth M. Mickey. "The Birthday Problem: Bayesian Inference with Multiple Discrete Hypotheses." In Bayesian Statistics for Beginners. Oxford University Press, 2019. http://dx.doi.org/10.1093/oso/9780198841296.003.0006.

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The “Birthday Problem” expands consideration from two hypotheses to multiple, discrete hypotheses. In this chapter, interest is in determining the posterior probability that a woman named Mary was born in a given month; there are twelve alternative hypotheses. Furthermore, consideration is given to assigning prior probabilities. The priors represent a priori probabilities that each alternative hypothesis is correct, where a priori means “prior to data collection,” and can be “informative” or “non-informative.” A Bayesian analysis cannot be conducted without using a prior distribution, whether

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van Erp H.R.N. and van Gelder P.H.A J.M. "Finding proper non-informative priors for regression coefficients." In Risk and Decision Analysis in Maintenance Optimization and Flood Management. IOS Press, 2009. https://doi.org/10.3233/978-1-60750-522-8-35.

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By using informational consistency requirements, Jaynes (1968) derives the form of maximal non-informative priors for regression coefficients, to be uniform. However, this result does not tell us what the limits of this uniform distribution should be. If we are faced with a problem of model selection this information is an integral part of the evidence, which is used to rank the various competing models. In this paper, we give some guidelines for choosing a parsimoneous proper uniform prior. It turns out that in order to construct such a parsimoneous prior one only needs to assign a maximal le

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GUTIéRREZ - PEñA, E., and M. MENDOZA. "Proper and non-informative conjugate priors for exponential family models." In Bayesian Theory and Applications. Oxford University Press, 2013. http://dx.doi.org/10.1093/acprof:oso/9780199695607.003.0019.

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Schlauch, Christian, Christian Wirth, and Nadja Klein. "Informed Spectral Normalized Gaussian Processes for Trajectory Prediction." In Frontiers in Artificial Intelligence and Applications. IOS Press, 2024. http://dx.doi.org/10.3233/faia240843.

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Prior parameter distributions provide an elegant way to represent prior expert knowledge for informed learning. Previous work has shown that using such informative priors to regularize probabilistic deep learning (DL) models increases their performance and data efficiency. However, commonly used sampling-based approximations for probabilistic DL models can be computationally expensive, requiring multiple forward passes and longer training times. Promising alternatives are compute efficient last layer kernel approximations like spectral normalized Gaussian processes (SNGPs). We propose a novel

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Arellano-Valle, Reinaldo B., Pilar L. Iglesias, and Ignacio Vidal. "Bayesian Inference for Elliptical Linear Models: Conjugate Analysis and Model Comparison." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0001.

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Abstract Bayesian inference for normal regression models, including sensitivity analysis, model comparison and error in variables under non-informative and conjugate prior for the model parameters has received considerable attention in the last decades. From a distributional point of view the results can be extended in several directions. One is by considering a wider class of prior distributions for the parameters of the model. Another, is by considering alternative distributions for the error terms. Usually, the results with non-conjugate priors rely heavily on MCMC methods. On the other han

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Araveeporn, Autcha. "The Non-Informative and Informative Prior Distributions in Estimating Variance Parameter of Normal Distribution." In Research Updates in Mathematics and Computer Science Vol. 4. B P International, 2024. http://dx.doi.org/10.9734/bpi/rumcs/v4/12439f.

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Conference papers on the topic "Non-informative priors"

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Sharma, Akash, Deep Joshi, and Nitin Chaudary. "Managing Uncertainty: Making Better Decisions In Gas Production Estimation Using Bayesian Approach." In SPE Eastern Regional Meeting. SPE, 2023. http://dx.doi.org/10.2118/215917-ms.

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Abstract This research paper investigates the effectiveness of Bayesian modeling techniques for estimating gas production variables in challenging low-data environments and complex systems, with a specific focus on the Haynesville Basin. The study aims to evaluate the superiority of Bayesian models in handling uncertainty and quantifying risks associated with gas production estimation by utilizing early-stage data and petrophysical information to determine initial production performance, b-factor, decline parameters, and EUR. The findings of this study provide valuable insights for industry de

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Meng, Ziqiao, Peilin Zhao, Yang Yu, and Irwin King. "Doubly Stochastic Graph-based Non-autoregressive Reaction Prediction." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/452.

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Organic reaction prediction is a critical task in drug discovery. Recently, researchers have achieved non-autoregressive reaction prediction by modeling the redistribution of electrons, resulting in state-of-the-art top-1 accuracy, and enabling parallel sampling. However, the current non-autoregressive decoder does not satisfy two essential rules of electron redistribution modeling simultaneously: the electron-counting rule and the symmetry rule. This violation of the physical constraints of chemical reactions impairs model performance. In this work, we propose a new framework called ReactionS

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Shergadwala, Murtuza N., and Jitesh H. Panchal. "Human Inductive Biases in Design Decision Making." In ASME 2020 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/detc2020-22252.

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Abstract Designers make information acquisition decisions, such as where to search and when to stop the search. Such decisions are typically made sequentially, such that at every search step designers gain information by learning about the design space. However, when designers begin acquiring information, their decisions are primarily based on their prior knowledge. Prior knowledge influences the initial set of assumptions that designers use to learn about the design space. These assumptions are collectively termed as inductive biases. Identifying such biases can help us better understand how

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Musharraf, Mashrura, Allison Moyle, Faisal Khan, and Brian Veitch. "Using Simulator Data to Facilitate Human Reliability Analysis in Offshore Emergency Situations." In ASME 2018 37th International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2018. http://dx.doi.org/10.1115/omae2018-78420.

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Data scarcity has always been a significant challenge in the domain of human reliability analysis (HRA). The advancement of simulation technologies provides opportunities to collect human performance data that can facilitate both the development and validation paradigms of HRA. The potential of simulator data to improve HRA can be tapped through the use of advanced machine learning tools like Bayesian methods. Except for Bayesian networks, Bayesian methods have not been widely used in the HRA community. This paper uses a Bayesian method to enhance human error probability (HEP) assessment in of

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Wada, Ryota, and Takuji Waseda. "Likelihood-Weighted Method of General Pareto Distribution for Extreme Wave Height Estimation." In ASME 2013 32nd International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2013. http://dx.doi.org/10.1115/omae2013-10792.

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In designing ocean structures, estimating the largest wave height it may encounter over its lifetime is a critical issue, but wave observation data is often sparse in space and time. Because of the limited data available, estimation errors are inevitably large. For an economical and robust structure design, the probability density function of the extreme wave height and its confidence interval must be theoretically quantified from limited information available. Extreme values estimations have been made by finding the best fitting distribution from limited observations, and extrapolating it for

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Sun, Yinbo, Lintao Ma, Yu Liu, et al. "Memory Augmented State Space Model for Time Series Forecasting." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/479.

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State space model (SSM) provides a general and flexible forecasting framework for time series. Conventional SSM with fixed-order Markovian assumption often falls short in handling the long-range temporal dependencies and/or highly non-linear correlation in time-series data, which is crucial for accurate forecasting. To this extend, we present External Memory Augmented State Space Model (EMSSM) within the sequential Monte Carlo (SMC) framework. Unlike the common fixed-order Markovian SSM, our model features an external memory system, in which we store informative latent state experience, whereb

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Flet-Berliac, Yannis, and Philippe Preux. "Only Relevant Information Matters: Filtering Out Noisy Samples To Boost RL." In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/376.

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In reinforcement learning, policy gradient algorithms optimize the policy directly and rely on sampling efficiently an environment. Nevertheless, while most sampling procedures are based on direct policy sampling, self-performance measures could be used to improve such sampling prior to each policy update. Following this line of thought, we introduce SAUNA, a method where non-informative transitions are rejected from the gradient update. The level of information is estimated according to the fraction of variance explained by the value function: a measure of the discrepancy between V and the em

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An, Dawn, Nam Ho Kim, and Jooho Choi. "In-Situ Monitoring and Prediction of Progressive Joint Wear Using Bayesian Statistics." In ASME 2010 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2010. http://dx.doi.org/10.1115/detc2010-29161.

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In this paper, a statistical methodology of estimating wear coefficient and predicting wear volume in a revolute joint using in-situ measurement data is presented. An instrumented slider-crank mechanism is built, which can measure the joint force and the relative motion between the pin and bushing during operation. The former is measured using a load cell built onto a necked portion of the hollow steel pin, while the latter is measured using a capacitance probe. In order to isolate the effect of friction in other joints, a porous carbon air bearing for the revolute joint between the follower l

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Merchante, Catharina, David Posé, Fernando Gallardo, Mar Quiñones, Juan Antonio Gálvez, and Beatriz Martínez-Poveda. "NEW ACTIVE METHODOLOGIES FOR CRITICAL LEARNING IN THE FIELD OF BIOCHEMISTRY OF HUMAN NUTRITION." In International Conference on Education and New Developments. inScience Press, 2021. http://dx.doi.org/10.36315/2021end142.

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Background: The teaching of the subject “Biochemical Basis of Human Nutrition” of the Degree in Biochemistry is based on the premise that students apply the knowledge acquired in previous courses concerning biochemistry and metabolism. However, for many topics covered in this subject, not rigorously application of this knowledge has been detected, existing influences derived from non-expert information available in the media. To a large extent, this problem lies in the fact that nutrition is a topic widely covered in the media, although often in a generalized, incomplete and not very rigorous

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