Relevant bibliographies by topics / Bayesian models

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Bayesian models'

Author: Grafiati
Published: 4 June 2021
Last updated: 8 June 2024

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the lists of relevant articles, books, theses, conference reports, and other scholarly sources on the topic 'Bayesian models.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Journal articles on the topic "Bayesian models"

1

McGlothlin, Anna E., and Kert Viele. "Bayesian Hierarchical Models." JAMA 320, no. 22 (December 11, 2018): 2365. http://dx.doi.org/10.1001/jama.2018.17977.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Figini, Silvia, Lijun Gao, and Paolo Giudici. "Bayesian operational risk models." Journal of Operational Risk 10, no. 2 (June 2015): 45–60. http://dx.doi.org/10.21314/jop.2015.155.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Furmańczyki, Konrad, and Wojciech Niemiro. "Sufficiency in bayesian models." Applicationes Mathematicae 25, no. 1 (1998): 113–20. http://dx.doi.org/10.4064/am-25-1-113-120.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Taylor, Greg. "BAYESIAN CHAIN LADDER MODELS." ASTIN Bulletin 45, no. 1 (October 17, 2014): 75–99. http://dx.doi.org/10.1017/asb.2014.25.

Full text
Abstract:
AbstractThe literature on Bayesian chain ladder models is surveyed. Both Mack and cross-classified forms of the chain ladder are considered. Both cases are examined in the context of error terms distributed according to a member of the exponential dispersion family. Tweedie and over-dispersed Poisson errors follow as special cases. Bayesian cross-classified chain ladder models may randomise row, column or diagonal parameters. Column and diagonal randomisation has been largely absent from the literature until recently. The present paper allows randomisation of row and column parameters. The Bayes estimator, the linear Bayes (credibility) estimator, and the MAP estimator are shown to be identical in the Mack case, and in the cross-classified case provided that the error terms are Tweedie distributed. In the Mack case the variance structure is generalised considerably from the existing literature. In the cross-classified case the model structure differs somewhat from the existing literature, and a comparison is made between the two. MAP estimators for the cross-classified case are often given by implicit equations that require numerical solution. Recursive formulas are given for these in the general case of error terms from the exponential dispersion family. The connection between the cross-classified case and Bornhuetter-Ferguson prediction is explored.
APA, Harvard, Vancouver, ISO, and other styles
5

Stamps, Judy A., and Willem E. Frankenhuis. "Bayesian Models of Development." Trends in Ecology & Evolution 31, no. 4 (April 2016): 260–68. http://dx.doi.org/10.1016/j.tree.2016.01.012.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Da-Silva, Cibele Queiroz, and Guilherme Souza Rodrigues. "Bayesian Dynamic Dirichlet Models." Communications in Statistics - Simulation and Computation 44, no. 3 (September 10, 2014): 787–818. http://dx.doi.org/10.1080/03610918.2013.795592.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

da-Silva, C. Q., H. S. Migon, and L. T. Correia. "Dynamic Bayesian beta models." Computational Statistics & Data Analysis 55, no. 6 (June 2011): 2074–89. http://dx.doi.org/10.1016/j.csda.2010.12.011.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Cochran, James J., Martin S. Levy, and Jeffrey D. Camm. "Bayesian coverage optimization models." Journal of Combinatorial Optimization 19, no. 2 (June 29, 2008): 158–73. http://dx.doi.org/10.1007/s10878-008-9172-y.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Grave de Peralta Menendez, Rolando, Amal Achaïbou, Pierre Bessière, Patrik Vuilleumier, and Sara Gonzalez Andino. "Bayesian Models of Mentalizing." Brain Topography 20, no. 4 (March 20, 2008): 278–83. http://dx.doi.org/10.1007/s10548-008-0047-4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Chater, Nick, Mike Oaksford, Ulrike Hahn, and Evan Heit. "Bayesian models of cognition." Wiley Interdisciplinary Reviews: Cognitive Science 1, no. 6 (May 11, 2010): 811–23. http://dx.doi.org/10.1002/wcs.79.

Full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Dissertations / Theses on the topic "Bayesian models"

1

Alharthi, Muteb. "Bayesian model assessment for stochastic epidemic models." Thesis, University of Nottingham, 2016. http://eprints.nottingham.ac.uk/33182/.

Full text
Abstract:
Acrucial practical advantage of infectious diseases modelling as a public health tool lies in its application to evaluate various disease-control policies. However, such evaluation is of limited use, unless a sufficiently accurate epidemic model is applied. If the model provides an adequate fit, it is possible to interpret parameter estimates, compare disease epidemics and implement control procedures. Methods to assess and compare stochastic epidemic models in a Bayesian framework are not well-established, particularly in epidemic settings with missing data. In this thesis, we develop novel methods for both model adequacy and model choice for stochastic epidemic models. We work with continuous time epidemic models and assume that only case detection times of infected individuals are available, corresponding to removal times. Throughout, we illustrate our methods using both simulated outbreak data and real disease data. Data augmented Markov Chain Monte Carlo (MCMC) algorithms are employed to make inference for unobserved infection times and model parameters. Under a Bayesian framework, we first conduct a systematic investigation of three different but natural methods of model adequacy for SIR (Susceptible-Infective-Removed) epidemic models. We proceed to develop a new two-stage method for assessing the adequacy of epidemic models. In this two stage method, two predictive distributions are examined, namely the predictive distribution of the final size of the epidemic and the predictive distribution of the removal times. The idea is based onlooking explicitly at the discrepancy between the observed and predicted removal times using the posterior predictive model checking approach in which the notion of Bayesian residuals and the and the posterior predictive p−value are utilized. This approach differs, most importantly, from classical likelihood-based approaches by taking into account uncertainty in both model stochasticity and model parameters. The two-stage method explores how SIR models with different infection mechanisms, infectious periods and population structures can be assessed and distinguished given only a set of removal times. In the last part of this thesis, we consider Bayesian model choice methods for epidemic models. We derive explicit forms for Bayes factors in two different epidemic settings, given complete epidemic data. Additionally, in the setting where the available data are partially observed, we extend the existing power posterior method for estimating Bayes factors to models incorporating missing data and successfully apply our missing-data extension of the power posterior method to various epidemic settings. We further consider the performance of the deviance information criterion (DIC) method to select between epidemic models.
APA, Harvard, Vancouver, ISO, and other styles
2

Volinsky, Christopher T. "Bayesian model averaging for censored survival models /." Thesis, Connect to this title online; UW restricted, 1997. http://hdl.handle.net/1773/8944.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Kim, Yong Ku. "Bayesian multiresolution dynamic models." Columbus, Ohio : Ohio State University, 2007. http://rave.ohiolink.edu/etdc/view?acc%5Fnum=osu1180465799.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Quintana, José Mario. "Multivariate Bayesian forecasting models." Thesis, University of Warwick, 1987. http://wrap.warwick.ac.uk/34805/.

Full text
Abstract:
This thesis concerns theoretical and practical Bayesian modelling of multivariate time series. Our main goal is to intruduce useful, flexible and tractable multivariate forecasting models and provide the necessary theory for their practical implementation. After a brief review of the dynamic linear model we formulate a new matrix-v-ariate generalization in which a significant part of the variance-covariance structure is unknown. And a new general algorithm, based on the sweep operator is provided for its recursive implementation. This enables important advances to be made in long-standing problems related with the specification of the variances. We address the problem of plug-in estimation and apply our results in the context of dynamic linear models. We extend our matrix-variate model by considering the unknown part of the variance-covariance structure to be dynamic. Furthermore, we formulate the dynamic recursive model which is a general counterpart of fully recursive econometric models. The latter part of the dissertation is devoted to modelling aspects. The usefulness of the methods proposed is illustrated with several examples involving real and simulated data.
APA, Harvard, Vancouver, ISO, and other styles
5

Kaufmann, Sylvia, and Sylvia Frühwirth-Schnatter. "Bayesian Analysis of Switching ARCH Models." Department of Statistics and Mathematics, WU Vienna University of Economics and Business, 2000. http://epub.wu.ac.at/744/1/document.pdf.

Full text
Abstract:
We consider a time series model with autoregressive conditional heteroskedasticity that is subject to changes in regime. The regimes evolve according to a multistate latent Markov switching process with unknown transition probabilities, and it is the constant in the variance process of the innovations that is subject to regime shifts. The joint estimation of the latent process and all model parameters is performed within a Bayesian framework using the method of Markov Chain Monte Carlo simulation. We perform model selection with respect to the number of states and the number of autoregressive parameters in the variance process using Bayes factors and model likelihoods. To this aim, the model likelihood is estimated by combining the candidate's formula with importance sampling. The usefulness of the sampler is demonstrated by applying it to the dataset previously used by Hamilton and Susmel who investigated models with switching autoregressive conditional heteroskedasticity using maximum likelihood methods. The paper concludes with some issues related to maximum likelihood methods, to classical model select ion, and to potential straightforward extensions of the model presented here. (author's abstract)
Series: Forschungsberichte / Institut für Statistik
APA, Harvard, Vancouver, ISO, and other styles
6

Vaidyanathan, Sivaranjani. "Bayesian Models for Computer Model Calibration and Prediction." The Ohio State University, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=osu1435527468.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Guo, Yixuan. "Bayesian Model Selection for Poisson and Related Models." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1439310177.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Gramacy, Robert B. "Bayesian treed Gaussian process models /." Diss., Digital Dissertations Database. Restricted to UC campuses, 2005. http://uclibs.org/PID/11984.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Husain, Syeda Tasmine. "Bayesian analysis of longitudinal models /." Internet access available to MUN users only, 2003. http://collections.mun.ca/u?/theses,163598.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ozbozkurt, Pelin. "Bayesian Inference In Anova Models." Phd thesis, METU, 2010. http://etd.lib.metu.edu.tr/upload/3/12611532/index.pdf.

Full text
Abstract:
Estimation of location and scale parameters from a random sample of size n is of paramount importance in Statistics. An estimator is called fully efficient if it attains the Cramer-Rao minimum variance bound besides being unbiased. The method that yields such estimators, at any rate for large n, is the method of modified maximum likelihood estimation. Apparently, such estimators cannot be made more efficient by using sample based classical methods. That makes room for Bayesian method of estimation which engages prior distributions and likelihood functions. A formal combination of the prior knowledge and the sample information is called posterior distribution. The posterior distribution is maximized with respect to the unknown parameter(s). That gives HPD (highest probability density) estimator(s). Locating the maximum of the posterior distribution is, however, enormously difficult (computationally and analytically) in most situations. To alleviate these difficulties, we use modified likelihood function in the posterior distribution instead of the likelihood function. We derived the HPD estimators of location and scale parameters of distributions in the family of Generalized Logistic. We have extended the work to experimental design, one way ANOVA. We have obtained the HPD estimators of the block effects and the scale parameter (in the distribution of errors)
they have beautiful algebraic forms. We have shown that they are highly efficient. We have given real life examples to illustrate the usefulness of our results. Thus, the enormous computational and analytical difficulties with the traditional Bayesian method of estimation are circumvented at any rate in the context of experimental design.
APA, Harvard, Vancouver, ISO, and other styles
More sources

Books on the topic "Bayesian models"

1

Kempthorne, Peter J. Bayesian parametric models. Cambridge, Mass: Alfred P. Sloan School of Management, Massachusetts Institute of Technology, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
2

Quintana, Jose Mario. Multivariate Bayesian forecasting models. [s.l.]: typescript, 1987.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
3

Barber, David, A. Taylan Cemgil, and Silvia Chiappa, eds. Bayesian Time Series Models. Cambridge: Cambridge University Press, 2009. http://dx.doi.org/10.1017/cbo9780511984679.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Barber, David. Bayesian time series models. Cambridge: Cambridge University Press, 2011.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
5

Hooten, Mevin B., and Trevor J. Hefley. Bringing Bayesian Models to Life. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Young, Simon Christopher. Bayesian models and repeated games. [s.l.]: typescript, 1989.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
7

West, Mike, and Jeff Harrison. Bayesian Forecasting and Dynamic Models. New York, NY: Springer New York, 1989. http://dx.doi.org/10.1007/978-1-4757-9365-9.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Congdon, Peter. Bayesian Models for Categorical Data. Chichester, UK: John Wiley & Sons, Ltd, 2005. http://dx.doi.org/10.1002/0470092394.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Weber, Philippe, and Christophe Simon. Benefits of Bayesian Network Models. Hoboken, NJ, USA: John Wiley & Sons, Inc., 2016. http://dx.doi.org/10.1002/9781119347316.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Bayesian analysis of linear models. New York: M. Dekker, 1985.

Find full text
APA, Harvard, Vancouver, ISO, and other styles
More sources

Book chapters on the topic "Bayesian models"

1

Otter, Thomas. "Bayesian Models." In Handbook of Market Research, 1–64. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-319-05542-8_24-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Hooten, Mevin B., and Trevor J. Hefley. "Bayesian Models." In Bringing Bayesian Models to Life, 3–9. Boca Raton, FL : CRC Press, Taylor & Francis Group, 2019.: CRC Press, 2019. http://dx.doi.org/10.1201/9780429243653-1.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Gulati, Sneh, and William J. Padgett. "Bayesian Models." In Parametric and Nonparametric Inference from Record-Breaking Data, 67–80. New York, NY: Springer New York, 2003. http://dx.doi.org/10.1007/978-0-387-21549-5_6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Otter, Thomas. "Bayesian Models." In Handbook of Market Research, 719–80. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-319-57413-4_24.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chakraborty, Ashis Kumar, Soumen Dey, Poulami Chakraborty, and Aleena Chanda. "Bayesian Models." In Springer Handbook of Engineering Statistics, 763–93. London: Springer London, 2012. http://dx.doi.org/10.1007/978-1-4471-7503-2_37.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Zwanzig, Silvelyn, and Rauf Ahmad. "Normal Linear Models." In Bayesian Inference, 126–82. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/9781003221623-6.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Sucar, Luis Enrique. "Bayesian Classifiers." In Probabilistic Graphical Models, 41–62. London: Springer London, 2015. http://dx.doi.org/10.1007/978-1-4471-6699-3_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Sucar, Luis Enrique. "Bayesian Classifiers." In Probabilistic Graphical Models, 43–69. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-61943-5_4.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Parametric Models." In Bayesian Survival Analysis, 30–46. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_2.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Ibrahim, Joseph G., Ming-Hui Chen, and Debajyoti Sinha. "Semiparametric Models." In Bayesian Survival Analysis, 47–99. New York, NY: Springer New York, 2001. http://dx.doi.org/10.1007/978-1-4757-3447-8_3.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Conference papers on the topic "Bayesian models"

1

von Toussaint, U. "Model Comparison of Avalanche Models." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 25th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2005. http://dx.doi.org/10.1063/1.2149830.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

Alabort-i-Medina, Joan, and Stefanos Zafeiriou. "Bayesian Active Appearance Models." In 2014 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, 2014. http://dx.doi.org/10.1109/cvpr.2014.439.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Chang, Ying-Lan, and Jen-Tzung Chien. "Bayesian nonparametric language models." In 2012 8th International Symposium on Chinese Spoken Language Processing (ISCSLP 2012). IEEE, 2012. http://dx.doi.org/10.1109/iscslp.2012.6423460.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Zhao, Hang, Sophia Gu, Chun Yu, and Xiaojun Bi. "Bayesian Hierarchical Pointing Models." In UIST '22: The 35th Annual ACM Symposium on User Interface Software and Technology. New York, NY, USA: ACM, 2022. http://dx.doi.org/10.1145/3526113.3545708.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Chandrashekar, Natesh, and Sundar Krishnamurty. "Bayesian Evaluation of Engineering Models." In ASME 2002 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/detc2002/dac-34141.

Full text
Abstract:
This paper deals with the development of simulation-based design models under uncertainty, and presents an approach for building surrogate models and validating them for their efficacy and relevance from a design decision perspective. Specifically, this work addresses the fundamental research issue of how to build such surrogate models that are computationally efficient and sufficiently accurate, and meaningful from the viewpoint of its subsequent use in design. Towards this goal, this work presents a Bayesian analysis based iterative model building and model validation process leading to reliable and accurate surrogate models, which can then be invoked in the final design optimization phase. The resulting surrogate models can be expected to act as abstractions or idealizations of the engineering analysis models and can mimic system performance in a computationally efficient manner to facilitate design decisions under uncertainty. This is accomplished by first building initial models, and then refining and validating them over many stages, in line with the iterative nature of the engineering design process. Salient features of this work include the introduction of a novel preference-based design screening strategy nested in an optimally-selected prior information set for validation purposes; and the use of a Bayesian evaluation based model-updating technique to capture new information and enhance model’s value and effectiveness. A case study of the design of a windshield wiper arm is used to demonstrate the overall methodology and the results are discussed.
APA, Harvard, Vancouver, ISO, and other styles
6

Yan, Xiaoran. "Bayesian model selection of stochastic block models." In 2016 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM). IEEE, 2016. http://dx.doi.org/10.1109/asonam.2016.7752253.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Bencomo, Nelly, and Luis H. Garcia Paucar. "RaM: Causally-Connected and Requirements-Aware Runtime Models using Bayesian Learning." In 2019 ACM/IEEE 22nd International Conference on Model Driven Engineering Languages and Systems (MODELS). IEEE, 2019. http://dx.doi.org/10.1109/models.2019.00005.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Luddecke, Daniel, Christoph Seidl, Jens Schneider, and Ina Schaefer. "Modeling user intentions for in-car infotainment systems using Bayesian networks." In 2015 ACM/IEEE 18th International Conference on Model-Driven Engineering Languages and Systems (MODELS). IEEE, 2015. http://dx.doi.org/10.1109/models.2015.7338269.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Hanson, K. M., G. S. Cunningham, and R. J. McKee. "Uncertainties in Bayesian geometric models." In Proceedings of 6th International Conference on Image Processing (ICIP'99). IEEE, 1999. http://dx.doi.org/10.1109/icip.1999.822851.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Haan and Godsill. "Bayesian models for DNA sequencing." In IEEE International Conference on Acoustics Speech and Signal Processing ICASSP-02. IEEE, 2002. http://dx.doi.org/10.1109/icassp.2002.1004800.

Full text
APA, Harvard, Vancouver, ISO, and other styles

Reports on the topic "Bayesian models"

1

Nadiga, Balasubramanya T., and Daniel Livescu. Bayesian Analysis of RANS Models. Office of Scientific and Technical Information (OSTI), June 2016. http://dx.doi.org/10.2172/1257091.

Full text
APA, Harvard, Vancouver, ISO, and other styles
2

George, Edward I., and Christian P. Robert. Capture-Recapture Models and Bayesian Sampling. Fort Belvoir, VA: Defense Technical Information Center, September 1990. http://dx.doi.org/10.21236/ada226853.

Full text
APA, Harvard, Vancouver, ISO, and other styles
3

Wayne, Martin. Hierarchical Bayesian Models for Assessing Reliability. DEVCOM Army Research Laboratory, August 2023. http://dx.doi.org/10.21236/ad1209640.

Full text
APA, Harvard, Vancouver, ISO, and other styles
4

Gelfand, Alan E., and Bani K. Mallick. Bayesian Analysis of Semiparametric Proportional Hazards Models. Fort Belvoir, VA: Defense Technical Information Center, March 1994. http://dx.doi.org/10.21236/ada279394.

Full text
APA, Harvard, Vancouver, ISO, and other styles
5

Griffiths, Thomas L. Theory-based Bayesian Models of Inductive Inference. Fort Belvoir, VA: Defense Technical Information Center, July 2010. http://dx.doi.org/10.21236/ada566965.

Full text
APA, Harvard, Vancouver, ISO, and other styles
6

Tenenbaum, Joshua B. Theory-Based Bayesian Models of Inductive Inference. Fort Belvoir, VA: Defense Technical Information Center, June 2010. http://dx.doi.org/10.21236/ada567195.

Full text
APA, Harvard, Vancouver, ISO, and other styles
7

Kitagawa, Toru, and Raffaella Giacomini. Robust Bayesian inference for set-identified models. The IFS, April 2020. http://dx.doi.org/10.1920/wp.cem.2020.1220.

Full text
APA, Harvard, Vancouver, ISO, and other styles
8

Giacomini, Raffaella, and Toru Kitagawa. Robust Bayesian inference for set-identified models. The IFS, November 2018. http://dx.doi.org/10.1920/wp.cem.2018.6118.

Full text
APA, Harvard, Vancouver, ISO, and other styles
9

Michaud, Isaac. Sequential Bayesian Methods for Analyzing Computer Models. Office of Scientific and Technical Information (OSTI), October 2023. http://dx.doi.org/10.2172/2202606.

Full text
APA, Harvard, Vancouver, ISO, and other styles
10

Moon, Hyungsik Roger, and Frank Schorfheide. Bayesian and Frequentist Inference in Partially Identified Models. Cambridge, MA: National Bureau of Economic Research, April 2009. http://dx.doi.org/10.3386/w14882.

Full text
APA, Harvard, Vancouver, ISO, and other styles
You might also be interested in the extended bibliographies on the topic 'Bayesian models' for particular source types:
Journal articles Dissertations / Theses Books
We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!

To the bibliography