Relevant bibliographies by topics / Bayesian Modeling

Contents

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

Academic literature on the topic 'Bayesian Modeling'

Author: Grafiati
Published: 4 June 2021
Last updated: 28 September 2024

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Journal articles on the topic "Bayesian Modeling"

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Qiao, Xin, and Hong Jiao. "Bayesian Psychometric Modeling." Measurement: Interdisciplinary Research and Perspectives 16, no. 2 (March 30, 2018): 135–37. http://dx.doi.org/10.1080/15366367.2018.1437307.

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Hicks, Tyler, Liliana Rodríguez-Campos, and Jeong Hoon Choi. "Bayesian Posterior Odds Ratios." American Journal of Evaluation 39, no. 2 (May 23, 2017): 278–89. http://dx.doi.org/10.1177/1098214017704302.

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To begin statistical analysis, Bayesians quantify their confidence in modeling hypotheses with priors. A prior describes the probability of a certain modeling hypothesis apart from the data. Bayesians should be able to defend their choice of prior to a skeptical audience. Collaboration between evaluators and stakeholders could make their choices more defensible. This article describes how evaluators and stakeholders could combine their expertise to select rigorous priors for analysis. The article first introduces Bayesian testing, then situates it within a collaborative framework, and finally illustrates the method with a real example.
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Gelman, Andrew. "Parameterization and Bayesian Modeling." Journal of the American Statistical Association 99, no. 466 (June 2004): 537–45. http://dx.doi.org/10.1198/016214504000000458.

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Ghosh, Subir. "Probability and Bayesian Modeling." Technometrics 62, no. 3 (July 2, 2020): 415–16. http://dx.doi.org/10.1080/00401706.2020.1783947.

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Robert, Christian, and Ioannis Ntzoufras. "Bayesian Modeling Using WinBUGS." CHANCE 25, no. 2 (April 16, 2012): 60–61. http://dx.doi.org/10.1080/09332480.2012.685377.

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Dunson, David B. "Bayesian nonparametric hierarchical modeling." Biometrical Journal 51, no. 2 (April 2009): 273–84. http://dx.doi.org/10.1002/bimj.200800183.

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Montes-Rojas, Gabriel, and Antonio F. Galvao. "Bayesian endogeneity bias modeling." Economics Letters 122, no. 1 (January 2014): 36–39. http://dx.doi.org/10.1016/j.econlet.2013.10.034.

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Ziegel, Eric. "Bayesian Thinking: Modeling and Computation." Technometrics 48, no. 4 (November 2006): 576–77. http://dx.doi.org/10.1198/tech.2006.s445.

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Kottas, Athanasios, and Alan E. Gelfand. "Bayesian Semiparametric Median Regression Modeling." Journal of the American Statistical Association 96, no. 456 (December 2001): 1458–68. http://dx.doi.org/10.1198/016214501753382363.

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Palacios, M. Blanca, and Mark F. J. Steel. "Non-Gaussian Bayesian Geostatistical Modeling." Journal of the American Statistical Association 101, no. 474 (June 1, 2006): 604–18. http://dx.doi.org/10.1198/016214505000001195.

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Dissertations / Theses on the topic "Bayesian Modeling"

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Joseph, Joshua Mason. "Nonparametric Bayesian behavior modeling." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/45263.

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Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Aeronautics and Astronautics, 2008.
Includes bibliographical references (p. 91-94).
As autonomous robots are increasingly used in complex, dynamic environments, it is crucial that the dynamic elements are modeled accurately. However, it is often difficult to generate good models due to either a lack of domain understanding or the domain being intractably large. In many domains, even defining the size of the model can be a challenge. While methods exist to cluster data of dynamic agents into common motion patterns, or "behaviors," assumptions of the number of expected behaviors must be made. This assumption can cause clustering processes to under-fit or over-fit the training data. In a poorly understood domain, knowing the number of expected behaviors a priori is unrealistic and in an extremely large domain, correctly fitting the training data is difficult. To overcome these obstacles, this thesis takes a Bayesian approach and applies a Dirichlet process (DP) prior over behaviors, which uses experience to reduce the likelihood of over-fitting or under-fitting the model complexity. Additionally, the DP maintains a probability mass associated with a novel behavior and can address countably infinite behaviors. This learning technique is applied to modeling agents driving in an urban setting. The learned DP-based driver behavior model is first demonstrated on a simulated city. Building on successful simulation results, the methodology is applied to GPS data of taxis driving around Boston. Accurate prediction of future vehicle behavior from the model is shown in both domains.
by Joshua Mason Joseph.
S.M.
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Turner, Brandon Michael. "Likelihood-Free Bayesian Modeling." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1316714657.

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Li, Feng. "Bayesian Modeling of Conditional Densities." Doctoral thesis, Stockholms universitet, Statistiska institutionen, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-89426.

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This thesis develops models and associated Bayesian inference methods for flexible univariate and multivariate conditional density estimation. The models are flexible in the sense that they can capture widely differing shapes of the data. The estimation methods are specifically designed to achieve flexibility while still avoiding overfitting. The models are flexible both for a given covariate value, but also across covariate space. A key contribution of this thesis is that it provides general approaches of density estimation with highly efficient Markov chain Monte Carlo methods. The methods are illustrated on several challenging non-linear and non-normal datasets. In the first paper, a general model is proposed for flexibly estimating the density of a continuous response variable conditional on a possibly high-dimensional set of covariates. The model is a finite mixture of asymmetric student-t densities with covariate-dependent mixture weights. The four parameters of the components, the mean, degrees of freedom, scale and skewness, are all modeled as functions of the covariates. The second paper explores how well a smooth mixture of symmetric components can capture skewed data. Simulations and applications on real data show that including covariate-dependent skewness in the components can lead to substantially improved performance on skewed data, often using a much smaller number of components. We also introduce smooth mixtures of gamma and log-normal components to model positively-valued response variables. In the third paper we propose a multivariate Gaussian surface regression model that combines both additive splines and interactive splines, and a highly efficient MCMC algorithm that updates all the multi-dimensional knot locations jointly. We use shrinkage priors to avoid overfitting with different estimated shrinkage factors for the additive and surface part of the model, and also different shrinkage parameters for the different response variables. In the last paper we present a general Bayesian approach for directly modeling dependencies between variables as function of explanatory variables in a flexible copula context. In particular, the Joe-Clayton copula is extended to have covariate-dependent tail dependence and correlations. Posterior inference is carried out using a novel and efficient simulation method. The appendix of the thesis documents the computational implementation details.

At the time of the doctoral defense, the following papers were unpublished and had a status as follows: Paper 3: In press. Paper 4: Manuscript.

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Rahlin, Alexandra Sasha. "Bayesian modeling of microwave foregrounds." Thesis, Massachusetts Institute of Technology, 2008. http://hdl.handle.net/1721.1/44735.

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Thesis (S.B.)--Massachusetts Institute of Technology, Dept. of Physics, 2008.
Includes bibliographical references (p. 93-94).
In the past decade, advances in precision cosmology have pushed our understanding of the evolving Universe to new limits. Since the discovery of the cosmic microwave background (CMB) radiation in 1965 by Penzias and Wilson, precise measurements of various cosmological parameters have provided a glimpse into the dynamics of the early Universe and the fate that awaits it in the very distant future. However, these measurements are hindered by the presence of strong foreground contamination (synchrotron, free-free, dust emission) from the interstellar medium in our own Galaxy and others that masks the CMB signal. Recent developments in modeling techniques may provide a better understanding of these foregrounds and allow improved constraints on current cosmological models. The method of nested sampling [16, 5], a Bayesian inference technique for calculating the evidence (the average of the likelihood over the prior mass), promises to be efficient and accurate for modeling the microwave foregrounds masking the CMB signal. An efficient and accurate algorithm would prove extremely useful for analyzing data obtained from current and future CMB experiments. This analysis aims to characterize the behavior of the nested sampling algorithm. We create a physically realistic data simulation, which we then use to reconstruct the CMB sky using both the Internal Linear Combination (ILC) method and nested sampling. The accuracy of the reconstruction is determined by figures of merit based on the RMS of the reconstruction, residuals and foregrounds. We find that modeling the foregrounds by nested sampling produces the most accurate results when the spectral index for the dust foreground component is fixed.
(cont.) Although the reconstructed foregrounds are qualitatively similar to what is expected, none of the non-linear models produce a CMB map as accurate as that produced by internal linear combination(ILC). More over, additional low-frequency components (synchrotron steepening, spinning dust) produce inconclusive results. Further study is needed to improve efficiency and accuracy of the nested sampling algorithm.
by Alexandra Sasha Rahlin.
S.B.
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Gao, Wenyu. "Advanced Nonparametric Bayesian Functional Modeling." Diss., Virginia Tech, 2020. http://hdl.handle.net/10919/99913.

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Functional analyses have gained more interest as we have easier access to massive data sets. However, such data sets often contain large heterogeneities, noise, and dimensionalities. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model, or developed from a more generic one by changing the prior distributions. Hence, this dissertation focuses on the development of Bayesian approaches for functional analyses due to their flexibilities. A nonparametric Bayesian approach, such as the Dirichlet process mixture (DPM) model, has a nonparametric distribution as the prior. This approach provides flexibility and reduces assumptions, especially for functional clustering, because the DPM model has an automatic clustering property, so the number of clusters does not need to be specified in advance. Furthermore, a weighted Dirichlet process mixture (WDPM) model allows for more heterogeneities from the data by assuming more than one unknown prior distribution. It also gathers more information from the data by introducing a weight function that assigns different candidate priors, such that the less similar observations are more separated. Thus, the WDPM model will improve the clustering and model estimation results. In this dissertation, we used an advanced nonparametric Bayesian approach to study functional variable selection and functional clustering methods. We proposed 1) a stochastic search functional selection method with application to 1-M matched case-crossover studies for aseptic meningitis, to examine the time-varying unknown relationship and find out important covariates affecting disease contractions; 2) a functional clustering method via the WDPM model, with application to three pathways related to genetic diabetes data, to identify essential genes distinguishing between normal and disease groups; and 3) a combined functional clustering, with the WDPM model, and variable selection approach with application to high-frequency spectral data, to select wavelengths associated with breast cancer racial disparities.
Doctor of Philosophy
As we have easier access to massive data sets, functional analyses have gained more interest to analyze data providing information about curves, surfaces, or others varying over a continuum. However, such data sets often contain large heterogeneities and noise. When generalizing the analyses from vectors to functions, classical methods might not work directly. This dissertation considers noisy information reduction in functional analyses from two perspectives: functional variable selection to reduce the dimensionality and functional clustering to group similar observations and thus reduce the sample size. The complicated data structures and relations can be easily modeled by a Bayesian hierarchical model due to its flexibility. Hence, this dissertation focuses on the development of nonparametric Bayesian approaches for functional analyses. Our proposed methods can be applied in various applications: the epidemiological studies on aseptic meningitis with clustered binary data, the genetic diabetes data, and breast cancer racial disparities.
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Caballero, Jose Louis Galan. "Modeling qualitative judgements in Bayesian networks." Thesis, Queen Mary, University of London, 2008. http://qmro.qmul.ac.uk/xmlui/handle/123456789/28170.

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Although Bayesian Networks (BNs) are increasingly being used to solve real world problems [47], their use is still constrained by the difficulty of constructing the node probability tables (NPTs). A key challenge is to construct relevant NPTs using the minimal amount of expert elicitation, recognising that it is rarely cost-effective to elicit complete sets of probability values. This thesis describes an approach to defining NPTs for a large class of commonly occurring nodes called ranked nodes. This approach is based on the doubly truncated Normal distribution with a central tendency that is invariably a type of a weighted function of the parent nodes. We demonstrate through two examples how to build large probability tables using the ranked nodes approach. Using this approach we are able to build the large probability tables needed to capture the complex models coming from assessing firm's risks in the safety or finance sector. The aim of the first example with the National Air-Traffic Services(NATS) is to show that using this approach we can model the impact of the organisational factors in avoiding mid-air aircraft collisions. The resulting model was validated by NATS and helped managers to assess the efficiency of the company handling risks and thus, control the likelihood of air-traffic incidents. In the second example, we use BN models to capture the operational risk (OpRisk) in financial institutions. The novelty of this approach is the use of causal reasoning as a means to reduce the uncertainty surrounding this type of risk. This model was validated against the Basel framework [160], which is the emerging international standard regulation governing how financial institutions assess OpRisks.
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Zhuang, Lili. "Bayesian Dynamical Modeling of Count Data." The Ohio State University, 2011. http://rave.ohiolink.edu/etdc/view?acc_num=osu1315949027.

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Nounou, Mohamed Numan. "Multiscale bayesian linear modeling and applications /." The Ohio State University, 2000. http://rave.ohiolink.edu/etdc/view?acc_num=osu1488203552781115.

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Harati, Nejad Torbati Amir Hossein. "Nonparametric Bayesian Approaches for Acoustic Modeling." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/338396.

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Electrical Engineering
Ph.D.
The goal of Bayesian analysis is to reduce the uncertainty about unobserved variables by combining prior knowledge with observations. A fundamental limitation of a parametric statistical model, including a Bayesian approach, is the inability of the model to learn new structures. The goal of the learning process is to estimate the correct values for the parameters. The accuracy of these parameters improves with more data but the model’s structure remains fixed. Therefore new observations will not affect the overall complexity (e.g. number of parameters in the model). Recently, nonparametric Bayesian methods have become a popular alternative to Bayesian approaches because the model structure is learned simultaneously with the parameter distributions in a data-driven manner. The goal of this dissertation is to apply nonparametric Bayesian approaches to the acoustic modeling problem in continuous speech recognition. Three important problems are addressed: (1) statistical modeling of sub-word acoustic units; (2) semi-supervised training algorithms for nonparametric acoustic models; and (3) automatic discovery of sub-word acoustic units. We have developed a Doubly Hierarchical Dirichlet Process Hidden Markov Model (DHDPHMM) with a non-ergodic structure that can be applied to problems involving sequential modeling. DHDPHMM shares mixture components between states using two Hierarchical Dirichlet Processes (HDP). An inference algorithm for this model has been developed that enables DHDPHMM to outperform both its hidden Markov model (HMM) and HDP HMM (HDPHMM) counterparts. This inference algorithm is shown to also be computationally less expensive than a comparable algorithm for HDPHMM. In addition to sharing data, the proposed model can learn non-ergodic structures and non-emitting states, something that HDPHMM does not support. This extension to the model is used to model finite length sequences. We have also developed a generative model for semi-supervised training of DHDPHMMs. Semi-supervised learning is an important practical requirement for many machine learning applications including acoustic modeling in speech recognition. The relative improvement in error rates on classification and recognition tasks is shown to be 22% and 7% respectively. Semi-supervised training results are slightly better than supervised training (29.02% vs. 29.71%). Context modeling was also investigated and results show a modest improvement of 1.5% relative over the baseline system. We also introduce a nonparametric Bayesian transducer based on an ergodic HDPHMM/DHDPHMM that automatically segments and clusters the speech signal using an unsupervised approach. This transducer was used in several applications including speech segmentation, acoustic unit discovery, spoken term detection and automatic generation of a pronunciation lexicon. For the segmentation problem, an F¬¬¬¬¬¬-score of 76.62% was achieved which represents a 9% relative improvement over the baseline system. On the spoken term detection tasks, an average precision of 64.91% was achieved, which represents a 20% improvement over the baseline system. Lexicon generation experiments also show automatically discovered units (ADU) generalize to new datasets. In this dissertation, we have established the foundation for applications of non-parametric Bayesian modeling to problems such as speech recognition that involve sequential modeling. These models allow a new generation of machine learning systems that adapt their overall complexity in a data-driven manner and yet preserve meaningful modalities in the data. As a result, these models improve generalization and offer higher performance at lower complexity.
Temple University--Theses
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Beierholm, Ulrik Ravnsborg Quartz Steven Quartz Steven. "Bayesian modeling of sensory cue combinations /." Diss., Pasadena, Calif. : California Institute of Technology, 2007. http://resolver.caltech.edu/CaltechETD:etd-05212007-172639.

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Books on the topic "Bayesian Modeling"

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Levy, Roy, and Robert J. Mislevy. Bayesian Psychometric Modeling. Boca Raton : Taylor & Francis Group, 2016. | Series: Chapman & Hall/CRC statistics in the social and behavioral sciences: Chapman and Hall/CRC, 2017. http://dx.doi.org/10.1201/9781315374604.

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Fox, Jean-Paul. Bayesian Item Response Modeling. New York, NY: Springer New York, 2010. http://dx.doi.org/10.1007/978-1-4419-0742-4.

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Dey, Dipak. Bayesian modeling in bioinformatics. Boca Raton: Chapman & Hall/CRC, 2010.

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Sridhar, Narasi, ed. Bayesian Network Modeling of Corrosion. Cham: Springer International Publishing, 2024. http://dx.doi.org/10.1007/978-3-031-56128-3.

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Dipak, Dey, and Rao C. Radhakrishna 1920-, eds. Bayesian thinking: Modeling and computation. Boston: Elsevier, 2005.

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Williams, Sharifa Zakiya. Bayesian Modeling for Mental Health Surveys. [New York, N.Y.?]: [publisher not identified], 2018.

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Fox, Jean-Paul. Bayesian item response modeling: Theory and applications. New York, NY: Springer, 2010.

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Hrafnkelsson, Birgir, ed. Statistical Modeling Using Bayesian Latent Gaussian Models. Cham: Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-39791-2.

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Szeliski, Richard. Bayesian Modeling of Uncertainty in Low-Level Vision. Boston, MA: Springer US, 1989. http://dx.doi.org/10.1007/978-1-4613-1637-4.

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Szeliski, Richard. Bayesian Modeling of Uncertainty in Low-Level Vision. Boston, MA: Springer US, 1989.

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Book chapters on the topic "Bayesian Modeling"

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Bonate, Peter L. "Bayesian Modeling." In Pharmacokinetic-Pharmacodynamic Modeling and Simulation, 391–427. Boston, MA: Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-9485-1_10.

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Albert, Jim, and Maria Rizzo. "Bayesian Modeling." In R by Example, 277–305. New York, NY: Springer New York, 2011. http://dx.doi.org/10.1007/978-1-4614-1365-3_12.

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Barbieri, Nicola, Giuseppe Manco, and Ettore Ritacco. "Bayesian Modeling." In Probabilistic Approaches to Recommendations, 53–85. Cham: Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-031-01906-7_3.

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Kroese, Dirk P., and Joshua C. C. Chan. "Bayesian Inference." In Statistical Modeling and Computation, 227–62. New York, NY: Springer New York, 2013. http://dx.doi.org/10.1007/978-1-4614-8775-3_8.

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Guo, Renkuan. "Bayesian Reliability Modeling." In International Encyclopedia of Statistical Science, 104–6. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-04898-2_137.

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Neal, Radford M. "Bayesian Mixture Modeling." In Maximum Entropy and Bayesian Methods, 197–211. Dordrecht: Springer Netherlands, 1992. http://dx.doi.org/10.1007/978-94-017-2219-3_14.

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Conati, Cristina. "Bayesian Student Modeling." In Studies in Computational Intelligence, 281–99. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010. http://dx.doi.org/10.1007/978-3-642-14363-2_14.

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Finch, W. Holmes, and Jocelyn E. Bolin. "Bayesian Multilevel Modeling." In Multilevel Modeling Using R, 167–98. 3rd ed. Boca Raton: Chapman and Hall/CRC, 2024. http://dx.doi.org/10.1201/b23166-9.

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Albert, Jim. "Hierarchical Modeling." In Bayesian Computation with R, 153–79. New York, NY: Springer New York, 2009. http://dx.doi.org/10.1007/978-0-387-92298-0_7.

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Martin, Osvaldo A., Ravin Kumar, and Junpeng Lao. "Bayesian Inference." In Bayesian Modeling and Computation in Python, 1–30. Boca Raton: Chapman and Hall/CRC, 2021. http://dx.doi.org/10.1201/9781003019169-1.

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Conference papers on the topic "Bayesian Modeling"

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Dogucu, Mine, and Alicia Johnson. "Supporting Bayesian Modeling With Visualizations." In Bridging the Gap: Empowering and Educating Today’s Learners in Statistics. International Association for Statistical Education, 2022. http://dx.doi.org/10.52041/iase.icots11.t6c2.

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With computational advances, Bayesian modeling is becoming more accessible. But because Bayesian thinking often differs from learners’ previous statistics training, it can be challenging for novice Bayesian learners to conceptualize and interpret the three major components of a Bayesian analysis: the prior, likelihood, and posterior. To this end, we developed an R package, bayesrules, which provides tools for exploring common introductory Bayesian models: beta-binomial, gamma-Poisson, and normal-normal. Specifically, within these model settings, the bayesrules functions provide an active learning opportunity to interact with the three Bayesian model components, as well as the effects of different model settings on the model results. We present here the package’s visualization functions and how they can be utilized in a statistics classroom.
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"Bayesian Learning and Modeling." In 2006 16th IEEE Signal Processing Society Workshop on Machine Learning for Signal Processing. IEEE, 2006. http://dx.doi.org/10.1109/mlsp.2006.275531.

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Sendin, Ivan, Thiago Queiroz, and Marcos Batista. "Bayesian Triangle Smoothing." In 3rd International Symposium on Uncertainty Quantification and Stochastic Modeling. Rio de Janeiro, Brazil: ABCM Brazilian Society of Mechanical Sciences and Engineering, 2015. http://dx.doi.org/10.20906/cps/usm-2016-0024.

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Sander, Jennifer, and Jurgen Beyerer. "Bayesian fusion: Modeling and application." In 2013 Workshop on Sensor Data Fusion: Trends, Solutions, Applications (SDF). IEEE, 2013. http://dx.doi.org/10.1109/sdf.2013.6698254.

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Burbine, Andrew, John Sturtevant, David Fryer, and Bruce W. Smith. "Bayesian inference for OPC modeling." In SPIE Advanced Lithography, edited by Andreas Erdmann and Jongwook Kye. SPIE, 2016. http://dx.doi.org/10.1117/12.2219707.

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Montesano, Luis, Manuel Lop, Alexandre Bernardino, and Jose Santos-Victor. "Modeling affordances using Bayesian networks." In 2007 IEEE/RSJ International Conference on Intelligent Robots and Systems. IEEE, 2007. http://dx.doi.org/10.1109/iros.2007.4399511.

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Rowicka, Małgorzata. "Bayesian modeling of protein interaction networks." In BAYESIAN INFERENCE AND MAXIMUM ENTROPY METHODS IN SCIENCE AND ENGINEERING: 24th International Workshop on Bayesian Inference and Maximum Entropy Methods in Science and Engineering. AIP, 2004. http://dx.doi.org/10.1063/1.1835224.

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Vera, Alberto, and Siddhartha Banerjee. "The Bayesian Prophet." In SIGMETRICS '19: ACM SIGMETRICS / International Conference on Measurement and Modeling of Computer Systems. New York, NY, USA: ACM, 2019. http://dx.doi.org/10.1145/3309697.3331518.

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Iseki, Toshio. "An Improved Stochastic Modeling for Bayesian Wave Estimation." In ASME 2012 31st International Conference on Ocean, Offshore and Arctic Engineering. American Society of Mechanical Engineers, 2012. http://dx.doi.org/10.1115/omae2012-83740.

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A modified Bayesian modeling procedure for wave estimation is proposed. In this method, errors in the estimates of ship response functions can be taken into account. In order to discuss the relationship between the minimum ABIC and the accuracy of the estimated wave parameters, the ABIC surfaces and the optimum area of the wave estimation are shown with respect to the two hyperparameters. As a result, the modified Bayesian modeling makes the ABIC surface smoother and can provide stable wave estimation. This concludes that the modified Bayesian modeling is reliable within a certain accuracy to estimate the wave parameters.
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Stutz, John C. "Experience With Bayesian Image Based Surface Modeling." 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/10.1063/1.2149798.

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Reports on the topic "Bayesian Modeling"

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Schultz, Martin T., Thomas D. Borrowman, and Mitchell J. Small. Bayesian Networks for Modeling Dredging Decisions. Fort Belvoir, VA: Defense Technical Information Center, October 2011. http://dx.doi.org/10.21236/ada552536.

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Barker, Kash. Sparse Event Modeling with Hierarchical Bayesian Kernel Methods. Fort Belvoir, VA: Defense Technical Information Center, January 2016. http://dx.doi.org/10.21236/ad1008781.

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Lawson, Andrew. Bayesian Spatial and Spatio-Temporal Modeling in R. Instats Inc., 2024. http://dx.doi.org/10.61700/jsdeeudk51kk31519.

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This workshop provides a comprehensive introduction to advanced techniques for analyzing spatial and spatio-temporal data. While the examples used will be primarily from the health sciences, this four-day hands-on workshop is designed to equip PhD students, professors, and professional researchers with the skills to conduct cutting-edge research in various fields, including Geography, Epidemiology, Public Health, Biostatistics, Ecology, Sociology, and Political Science.
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Hauzenberger, Niko, Florian Huber, Gary Koop, and James Mitchell. Bayesian modeling of time-varying parameters using regression trees. Federal Reserve Bank of Cleveland, January 2023. http://dx.doi.org/10.26509/frbc-wp-202305.

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In light of widespread evidence of parameter instability in macroeconomic models, many time-varying parameter (TVP) models have been proposed. This paper proposes a nonparametric TVP-VAR model using Bayesian additive regression trees (BART). The novelty of this model stems from the fact that the law of motion driving the parameters is treated nonparametrically. This leads to great flexibility in the nature and extent of parameter change, both in the conditional mean and in the conditional variance. In contrast to other nonparametric and machine learning methods that are black box, inference using our model is straightforward because, in treating the parameters rather than the variables nonparametrically, the model remains conditionally linear in the mean. Parsimony is achieved through adopting nonparametric factor structures and use of shrinkage priors. In an application to US macroeconomic data, we illustrate the use of our model in tracking both the evolving nature of the Phillips curve and how the effects of business cycle shocks on inflationary measures vary nonlinearly with movements in uncertainty.
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Bugg, Julie, Joshua Clifford, Nicole Murchison, and Christina Ting. Instantiation of HCML Demonstrating Bayesian Predictive Modeling for Attentional Control. Office of Scientific and Technical Information (OSTI), April 2022. http://dx.doi.org/10.2172/1863278.

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Crews, John H., and Ralph C. Smith. Modeling and Bayesian Parameter Estimation for Shape Memory Alloy Bending Actuators. Fort Belvoir, VA: Defense Technical Information Center, February 2012. http://dx.doi.org/10.21236/ada556967.

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STIEN, Marita, Maren DRANGE-ESPELAND, and Ragnar HAUGE. On using Bayesian networks for modeling dependencies between prospects in oil exploration. Cogeo@oeaw-giscience, September 2011. http://dx.doi.org/10.5242/iamg.2011.0082.

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Mitrani, J. An Investigation Into Bayesian Networks for Modeling National Ignition Facility Capsule Implosions. Office of Scientific and Technical Information (OSTI), August 2008. http://dx.doi.org/10.2172/973637.

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Christakos, George, and Marc Serre. Modeling and Prediction of Space/Time Natural Processes Using A Bayesian Maximum Entropy. Fort Belvoir, VA: Defense Technical Information Center, May 2003. http://dx.doi.org/10.21236/ada424350.

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Baluga, Anthony, and Masato Nakane. Maldives Macroeconomic Forecasting:. Asian Development Bank, December 2020. http://dx.doi.org/10.22617/wps200431-2.

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This study aims to build an efficient small-scale macroeconomic forecasting tool for Maldives. Due to significant limitations in data availability, empirical economic modeling for the country can be problematic. To address data constraints and circumvent the “curse of dimensionality,” Bayesian vector autoregression estimations are utilized comprising of component-disaggregated domestic sectoral production, price, and tourism variables. Results demonstrate how this methodology is appropriate for economic modeling in Maldives. With the appropriate level of shrinkage, Bayesian vector autoregressions can exploit the information content of the macroeconomic and tourism variables. Augmenting for qualitative assessments, the directional inclination of the forecasts is improved.
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