Relevant bibliographies by topics / Bayesian mixing models

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Academic literature on the topic 'Bayesian mixing models'

Author: Grafiati

Published: 5 June 2025

Last updated: 1 August 2025

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Journal articles on the topic "Bayesian mixing models"

Erhardt, Erik B., and Edward J. Bedrick. "A Bayesian framework for stable isotope mixing models." Environmental and Ecological Statistics 20, no. 3 (2012): 377–97. http://dx.doi.org/10.1007/s10651-012-0224-1.

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Stock, Brian C., Andrew L. Jackson, Eric J. Ward, Andrew C. Parnell, Donald L. Phillips, and Brice X. Semmens. "Analyzing mixing systems using a new generation of Bayesian tracer mixing models." PeerJ 6 (June 21, 2018): e5096. http://dx.doi.org/10.7717/peerj.5096.

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The ongoing evolution of tracer mixing models has resulted in a confusing array of software tools that differ in terms of data inputs, model assumptions, and associated analytic products. Here we introduce MixSIAR, an inclusive, rich, and flexible Bayesian tracer (e.g., stable isotope) mixing model framework implemented as an open-source R package. Using MixSIAR as a foundation, we provide guidance for the implementation of mixing model analyses. We begin by outlining the practical differences between mixture data error structure formulations and relate these error structures to common mixing

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Walker, Stephen G., and Bani K. Mallick. "Hierarchical Generalized Linear Models and Frailty Models with Bayesian Nonparametric Mixing." Journal of the Royal Statistical Society: Series B (Statistical Methodology) 59, no. 4 (1997): 845–60. http://dx.doi.org/10.1111/1467-9868.00101.

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Osada, Yutaka, Jun Matsubayashi, and Ichiro Tayasu. "Diagnosing underdetermination in stable isotope mixing models." PLOS ONE 16, no. 10 (2021): e0257818. http://dx.doi.org/10.1371/journal.pone.0257818.

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Stable isotope mixing models (SIMMs) provide a powerful methodology for quantifying relative contributions of several sources to a mixture. They are widely used in the fields of ecology, geology, and archaeology. Although SIMMs have been rapidly evolved in the Bayesian framework, the underdetermination of mixing space remains problematic, i.e., the estimated relative contributions are incompletely identifiable. Here we propose a statistical method to quantitatively diagnose underdetermination in Bayesian SIMMs, and demonstrate the applications of our method (named β-dependent SIMM) using two m

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Zhou, Shijie, Andre E. Punt, Roy Deng, Marco Kienzle, and Wayne Rochester. "Bayesian fishable biomass dynamics models incorporating fished area and relative fish density." Canadian Journal of Fisheries and Aquatic Sciences 68, no. 9 (2011): 1603–14. http://dx.doi.org/10.1139/f2011-083.

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Fisheries typically experience large changes over time in fishing effort. The size of the area fished may also change substantially over time, mimicking the trend in fishing effort, and may have major effects on the population dynamics and fishing process. We extend a biomass dynamics model to incorporate fished area and relative fish density in fished and unfished areas. The fishable population is defined as those individuals in the fished area and those that are sufficiently close to the fished area that they could potentially move into fished area during the fishing season. We estimate fish

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Chen, Cathy W. S., Richard Gerlach, and Edward M. H. Lin. "Bayesian estimation of smoothly mixing time-varying parameter GARCH models." Computational Statistics & Data Analysis 76 (August 2014): 194–209. http://dx.doi.org/10.1016/j.csda.2013.09.019.

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Semmens, Brice X., Jonathan W. Moore, and Eric J. Ward. "Improving Bayesian isotope mixing models: a response to Jacksonet al.(2009)." Ecology Letters 12, no. 3 (2009): E6—E8. http://dx.doi.org/10.1111/j.1461-0248.2009.01283.x.

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Franco-Trecu, Valentina, Massimiliano Drago, Federico G. Riet-Sapriza, Andrew Parnell, Rosina Frau, and Pablo Inchausti. "Bias in Diet Determination: Incorporating Traditional Methods in Bayesian Mixing Models." PLoS ONE 8, no. 11 (2013): e80019. http://dx.doi.org/10.1371/journal.pone.0080019.

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Knock, E. S., and P. D. O'Neill. "Bayesian model choice for epidemic models with two levels of mixing." Biostatistics 15, no. 1 (2013): 46–59. http://dx.doi.org/10.1093/biostatistics/kxt023.

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Norets, Andriy, and Justinas Pelenis. "POSTERIOR CONSISTENCY IN CONDITIONAL DENSITY ESTIMATION BY COVARIATE DEPENDENT MIXTURES." Econometric Theory 30, no. 3 (2013): 606–46. http://dx.doi.org/10.1017/s026646661300042x.

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This paper considers Bayesian nonparametric estimation of conditional densities by countable mixtures of location-scale densities with covariate dependent mixing probabilities. The mixing probabilities are modeled in two ways. First, we consider finite covariate dependent mixture models, in which the mixing probabilities are proportional to a product of a constant and a kernel and a prior on the number of mixture components is specified. Second, we consider kernel stick-breaking processes for modeling the mixing probabilities. We show that the posterior in these two models is weakly and strong

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Dissertations / Theses on the topic "Bayesian mixing models"

Cooper, Richard. "Advancing methods for apportioning the sources of sediment in rivers : combining spectroscopy and stable isotopes with Bayesian mixing models." Thesis, University of East Anglia, 2015. https://ueaeprints.uea.ac.uk/53376/.

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Sediment fingerprinting is a commonly employed technique for estimating sediment contributions from various eroding terrestrial sources to fluvial sediment load via a mixing model approach. However, there remain significant shortcomings in sediment fingerprinting practice, specifically relating to difficulties in producing high-temporal resolution apportionment estimates, inconsistencies in mixing model uncertainty representation, and a lack of attention given to organic matter provenance. Addressing these deficiencies, a combined X-ray fluorescence spectroscopy (XRFS) and diffuse reflectance

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Bergström, Johannes. "Models in Neutrino Physics : Numerical and Statistical Studies." Doctoral thesis, KTH, Teoretisk partikelfysik, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-127409.

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The standard model of particle physics can excellently describe the vast majorityof data of particle physics experiments. However, in its simplest form, it cannot account for the fact that the neutrinos are massive particles and lepton flavorsmixed, as required by the observation of neutrino oscillations. Hence, the standardmodel must be extended in order to account for these observations, opening up thepossibility to explore new and interesting physical phenomena. There are numerous models proposed to accommodate massive neutrinos. Thesimplest of these are able to describe the observations us

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Connell, Matthew. "Bayesian Model Mixing for Extrapolation from an EFT Toy." Ohio University Honors Tutorial College / OhioLINK, 2021. http://rave.ohiolink.edu/etdc/view?acc_num=ouhonors1619122381888487.

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Hamilton, Erin Kinzel. "Multiscale and meta-analytic approaches to inference in clinical healthcare data." Diss., Georgia Institute of Technology, 2013. http://hdl.handle.net/1853/47600.

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The field of medicine is regularly faced with the challenge of utilizing information that is complicated or difficult to characterize. Physicians often must use their best judgment in reaching decisions or recommendations for treatment in the clinical setting. The goal of this thesis is to use innovative statistical tools in tackling three specific challenges of this nature from current healthcare applications. The first aim focuses on developing a novel approach to meta-analysis when combining binary data from multiple studies of paired design, particularly in cases of high heterogeneity bet

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

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Dans cette thèse nous avons traité le problème de l'estimation non paramétrique des lois de probabilités. Dans un premier temps, nous avons supposé que la densité inconnue f a été approchée par un mélange de base B-spline quadratique. Puis, nous avons proposé un nouvel estimateur de la densité inconnue f basé sur les fonctions B-splines quadratiques, avec deux méthodes d'estimation. La première est base sur la méthode du maximum de vraisemblance et la deuxième est basée sur la méthode d'estimation Bayésienne MAP. Ensuite, nous avons généralisé notre étude d'estimation dans le cadre du mélange

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Blanchard, J. Wade. "INFERENCE ON THE DIET COMPOSITION OF PREDATORS USING FATTY ACID SIGNATURES: AN APPLICATION OF BAYESIAN INFERENCE ON LINEAR MIXING MODELS." 2011. http://hdl.handle.net/10222/13329.

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Determining the diet composition of predators is an important ingredient in many areas of ecology: understanding predator prey relationships, foraging behaviour of predators and consumption models to name a few. Iverson et al. (2004) developed a method based on the fatty acid signatures known as quantitative fatty acid signature analysis (QFASA). Fatty acids are the basic building blocks of most lipids and are indicative of diet, in the sense that higher level predators have limited ability to modify the fatty acids they ingest. Billheimer (2001) introduced a Bayesian compositional receptor mo

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Book chapters on the topic "Bayesian mixing models"

Stoura, Charikleia D., Konstantinos E. Tatsis, and Eleni N. Chatzi. "Rail Roughness Profile Identification from Vibration Data via Mixing of Reduced-Order Train Models and Bayesian Filtering." In Model Validation and Uncertainty Quantification, Volume 3. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37003-8_11.

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Togashi, Hiroyuki, Yukinori Nakane, Yosuke Amano, and Yutaka Kurita. "Estimating the Diets of Fish Using Stomach Contents Analysis and a Bayesian Stable Isotope Mixing Models in Sendai Bay." In Oceanography Challenges to Future Earth. Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-00138-4_19.

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Lebek, Karen, and Tobias Krueger. "12. On the dialogue between ethnographic field work and statistical modelling." In Critical Physical Geography: Interdisciplinary Approaches to Nature, Power and Politics. Open Book Publishers, 2025. https://doi.org/10.11647/obp.0418.12.

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Ethnographic and statistical methods have very different epistemological underpinnings; ethnographic research is interpretive, while statistical research is generalizing. Our case study combines these two contrasting approaches and brings them into an eye-level conversation with each other. We conceptualized a Bayesian statistical model for Rainwater Harvesting Mode in rural South Africa, based on hypothetical relations derived from ethnographic field observations. The model pointed to spurious relations and that new hypotheses from fieldwork helped explain. Mixing the two methods means using

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Cotrufo, M. Francesca, and Yamina Pressler. "Isotope Mixing." In A Primer on Stable Isotopes in Ecology. Oxford University PressOxford, 2023. http://dx.doi.org/10.1093/oso/9780198854494.003.0004.

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Abstract “Isotope Mixing” reviews principles and applications of stable isotope mixing models in ecology. The chapter describes how ecologists developed stable isotope mixing models to study source partitioning by applying the principle of mass balance. Equations for the mathematical solution of two- and three-pool mixing models, using one and two isotopes respectively, are presented. The chapter expands upon approaches for accounting for fractionation and elemental concentration in isotope mixing models. The chapter explores the Keeling plot method for quantifying the isotopic composition of

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Dethlefsen, Claus. "Markov Random Field Extensions using State Space Models." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0029.

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Abstract The class of state space models is very broad and comprises structural time series models, ARIMA models, cubic spline models, and, as demonstrated by Lavine (1999), also Markov random field models. The Kalman filter techniques are powerful tools for inference in such sequential models. Basic references on state space model methodology are Harvey (1989), West and Harrison (1997) and Durbin and Koopman (2001). In the past decade, there has been a certain interest in developing Markov chain Monte Carlo (MCMC) methods for the analysis of complex state state space models (see Carlin et al.

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Florens, J. P., M. Mouchart, and J. M. Rolin. "Bayesian Analysis of Mixtures: Some Results on Exact Estimability and Identification." In Bayesian Statistics 4. Oxford University PressOxford, 1992. http://dx.doi.org/10.1093/oso/9780198522669.003.0008.

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Abstract Models frequently used for car insurance portfolios assume that the distribution of the expected number of accidents caused by a client is a mixture of Dirichlet processes. Given that the number of clients is typically rather large, insurance companies may find it relevant to inquire whether the posterior distributions (given the number of individual accidents) of the mixture, of the mixing parameter and the predictive distribution of a new observation, given the number of individual accidents, are consistently convergent. For most models of this rather general class, answering these

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Lee, Jonathan, and Alex Lenkoski. "Incorporating Model Uncertainty in Market Response Models with Multiple Endogenous Variables by Bayesian Model Averaging." In Business, Management and Economics. IntechOpen, 2022. http://dx.doi.org/10.5772/intechopen.108927.

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We develop a method to incorporate model uncertainty by model averaging in generalized linear models subject to multiple endogeneity and instrumentation. Our approach builds on a Gibbs sampler for the instrumental variable framework that incorporates model uncertainty in both outcome and instrumentation stages. Direct evaluation of model probabilities is intractable in this setting. However, we show that by nesting model moves inside the Gibbs sampler, a model comparison can be performed via conditional Bayes factors, leading to straightforward calculations. This new Gibbs sampler is slightly

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Scott, Steven L., and Padhraic Smyth. "The Markov Modulated Poisson Process and Markov Poisson Cascade with Applications to Web Traffic Modeling." In Bayesian Statistics 7. Oxford University PressOxford, 2003. http://dx.doi.org/10.1093/oso/9780198526155.003.0047.

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Abstract A Markov modulated Poisson Process (MMPP) is a Poisson process whose rate varies according to a Markov process. The nonhomogeneous MMPP developed in this article is a natural model for point processes whose events combine irregular bursts of activity with predictable (e.g. daily and hourly) patterns. We show how the MMPP may be viewed as a superposition of unobserved Poisson processes that are activated and deactivated by an unobserved Markov process. The MMPP is a continuous time model which may also be viewed as a discretely indexed nonstationary hidden Markov model by viewing inter

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Anye Cho, Bovinille. "Surrogate and Multiscale Modelling for (Bio)reactor Scale-up and Visualisation." In Machine Learning and Hybrid Modelling for Reaction Engineering. Royal Society of Chemistry, 2023. http://dx.doi.org/10.1039/bk9781837670178-00277.

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Bioresource production in bioreactors presents a sustainable biotechnology for tackling the ever-increasing energy and mass demands of the world’s surging population. To attain commercial viability, reaction engineers must efficiently design and upscale these bioreactors for the industrial production of high value biochemicals, fuels, and materials. These engineers utilise computational fluid dynamics (CFD) to visualise bioreactor fluid flow and optimise dead zones with poor mixing, leading to promising bioreactor configurations. An advanced route, yet to be widely deployed, is the integration

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Conference papers on the topic "Bayesian mixing models"

Amiri, Fahime, and Mohammad Hossein Kahaei. "New Bayesian approach for semi-supervised hyperspectral unmixing in linear mixing models." In 2017 Iranian Conference on Electrical Engineering (ICEE). IEEE, 2017. http://dx.doi.org/10.1109/iraniancee.2017.7985334.

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Wang, Ruohui, and Dahua Lin. "Scalable Estimation of Dirichlet Process Mixture Models on Distributed Data." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/646.

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We consider the estimation of Dirichlet Process Mixture Models (DPMMs) in distributed environments, where data are distributed across multiple computing nodes. A key advantage of Bayesian nonparametric models such as DPMMs is that they allow new components to be introduced on the fly as needed. This, however, posts an important challenge to distributed estimation -- how to handle new components efficiently and consistently. To tackle this problem, we propose a new estimation method, which allows new components to be created locally in individual computing nodes. Components corresponding to the

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Pourgol-Mohamad, Mohammad. "Thermal Hydraulics Structural Uncertainty Analysis: Approaches and Challenges." In ASME 2010 3rd Joint US-European Fluids Engineering Summer Meeting collocated with 8th International Conference on Nanochannels, Microchannels, and Minichannels. ASMEDC, 2010. http://dx.doi.org/10.1115/fedsm-icnmm2010-31263.

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Model uncertainty is a relatively new topic of discussion in TH code calculations, despite being often the major contributor to the overall uncertainty and a challenging practice in uncertainty analysis. The Integrated thermal-hydraulics uncertainty analysis (IMTHUA) methodology, developed by the authors, treats the TH code structural uncertainties (generally known as model uncertainty) explicitly by treating internal sub-model uncertainties, and by propagating such model uncertainties in the code calculations, including uncertainties about input parameters. This paper presents systematic mode

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Song, Xiuyao, Chris Jermaine, Sanjay Ranka, and John Gums. "A bayesian mixture model with linear regression mixing proportions." In the 14th ACM SIGKDD international conference. ACM Press, 2008. http://dx.doi.org/10.1145/1401890.1401970.

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Ettler, Pavel, Ivan Puchr, and Kamil Dedecius. "Bayesian model mixing for cold rolling mills: Test results." In 2013 International Conference on Process Control (PC). IEEE, 2013. http://dx.doi.org/10.1109/pc.2013.6581437.

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Mead, Griffin J., Daniel I. Herman, Fabrizio R. Giorgetta, et al. "Emissions Inventory Optimization using Sector-Resolved Dual-Comb Methane Observations." In Optics and Photonics for Sensing the Environment. Optica Publishing Group, 2023. http://dx.doi.org/10.1364/es.2023.em1e.4.

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Dynamic linear model analysis of dual-comb and CRDS methane, ethane, and ammonia measurements extracts sector-resolved methane mixing ratios. From these data, a Bayesian inversion retrieves sector specific energy and agriculture methane fluxes in Northern Colorado.

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Luo, Hanyu, and Xiaojing Liu. "Inverse Uncertainty Quantification for Subchannel Code With PSBT Experimental Benchmark." In 2024 31st International Conference on Nuclear Engineering. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/icone31-135192.

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Abstract Inverse uncertainty quantification analysis for subchannel programs has been conducted to decide uncertainty distribution of important model parameters based on the modified Markov Chain Monte Carlo (MCMC) algorithm using the PSBT void fraction experiment. The posterior probability distribution expression for input parameter uncertainty can be derived using Bayesian principles, typically representing a proportional relationship. However, obtaining the direct normalization coefficient proves challenging, prompting the use of the MCMC algorithm to address this issue. Given the substanti

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Tekgul, Bulut, I.-Han Liu, Manohar Vittal, et al. "Design Optimization of an Ethanol Heavy-Duty Engine Using Design of Experiments and Bayesian Optimization." In ASME 2022 ICE Forward Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/icef2022-90257.

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Abstract Diesel-fueled engines still hold a large market share in the medium and heavy-duty transportation sector. However, the increase in fossil fuel prices and the strict emission regulations are leading engine manufacturers to seek cleaner alternatives without a compromise in performance. Alcohol-based fuels, such as ethanol, offer a promising alternative to diesel fuel in meeting regulatory demands. Ethanol provides cleaner combustion and lower levels of soot due to its chemical properties, in particular its lower level of carbon content. In addition, the stoichiometric operating conditio

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Jackson, Richard W., Dario Luberti, Hui Tang, et al. "Measurement and Analysis of Buoyancy-Induced Heat Transfer in Aero-Engine Compressor Rotors." In ASME Turbo Expo 2020: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/gt2020-16219.

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Abstract The flow inside cavities between co-rotating compressor discs of aero-engines is driven by buoyancy, with Grashof numbers exceeding 1013. This phenomenon creates a conjugate problem: the Nusselt numbers depend on the radial temperature distribution of the discs, and the disc temperatures depend on the Nusselt numbers. Furthermore, Coriolis forces in the rotating fluid generate cyclonic and anti-cyclonic circulations inside the cavity. Such flows are three-dimensional, unsteady and unstable, and it is a challenge to compute and measure the heat transfer from the discs to the axial thro

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Zhengliang, Yu. "Carbon sink effect caused by chemical weathering is weak in mountainous catchments of the west Nyaingêntanglha Ranges, Central Tibetan Plateau: Evidence from Bayesian mixing model using δ¹³C_DIC". У Goldschmidt2022. European Association of Geochemistry, 2022. http://dx.doi.org/10.46427/gold2022.10066.

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Reports on the topic "Bayesian mixing models"

Jackson, Andrew. Stable Isotope Mixing Models in R. Instats Inc., 2024. https://doi.org/10.61700/f9hpz9exdbdpu1420.

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This four-day seminar provides an intensive exploration of Stable Isotope Mixing Models (SIMMs), equipping participants with practical skills for analyzing ecological data using advanced R tools like SIBER, simmr, and MixSIAR. The workshop covers Bayesian modeling techniques, guiding researchers to proficiently apply and interpret SIMMs for robust ecological insights in real-world datasets.

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