Thematische Bibliographien / Bayesian estimate

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Auswahl der wissenschaftlichen Literatur zum Thema „Bayesian estimate“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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Zeitschriftenartikel zum Thema "Bayesian estimate"

Chang, Jack, Dhara Patel, Kimberly C. Claeys, Marc H. Scheetz, and Emily Heil. "1090. Does calculation method matter for targeting vancomycin AUC?" Open Forum Infectious Diseases 8, Supplement_1 (2021): S636. http://dx.doi.org/10.1093/ofid/ofab466.1284.

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Abstract Background Recent vancomycin (VAN) guidelines recommend targeting an area under the curve (AUC) concentration of 400-600 for treatment of methicillin resistant Staphylococcus aureus infections. Multiple strategies for calculating AUC exist, including first order pharmacokinetic (foPK) equations and Bayesian models. Most clinical applications of foPK assume unchanged patient status and project ideal administration times to estimate exposure. Bayesian modeling provides the best estimate of true drug exposure and can incorporate changing patient covariates and exact doses. We compared tw

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Rahman, Mohammad Lutfor, Steven G. Gilmour, Peter J. Zemroch, and Pauline R. Ziman. "Bayesian analysis of fuel economy experiments." Journal of Statistical Research 54, no. 1 (2020): 43–63. http://dx.doi.org/10.47302/jsr.2020540103.

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Statistical analysts can encounter difficulties in obtaining point and interval estimates for fixed effects when sample sizes are small and there are two or more error strata to consider. Standard methods can lead to certain variance components being estimated as zero which often seems contrary to engineering experience and judgement. Shell Global Solutions (UK) has encountered such challenges and is always looking for ways to make its statistical techniques as robust as possible. In this instance, the challenge was to estimate fuel effects and confidence limits from small-sample fuel economy

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Sanger, Terence D. "Bayesian Filtering of Myoelectric Signals." Journal of Neurophysiology 97, no. 2 (2007): 1839–45. http://dx.doi.org/10.1152/jn.00936.2006.

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Surface electromyography is used in research, to estimate the activity of muscle, in prosthetic design, to provide a control signal, and in biofeedback, to provide subjects with a visual or auditory indication of muscle contraction. Unfortunately, successful applications are limited by the variability in the signal and the consequent poor quality of estimates. I propose to use a nonlinear recursive filter based on Bayesian estimation. The desired filtered signal is modeled as a combined diffusion and jump process and the measured electromyographic (EMG) signal is modeled as a random process wi

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Roy, Himadri Shekhar, Amrit Kumar Paul, Ranjit Kumar Paul, Ramesh Kumar Singh, MD `YEASIN, and Prakash Kumar. "Estimation of Heritability of Karan Fries Cattle using Bayesian Procedure." Indian Journal of Animal Sciences 92, no. 5 (2022): 645–48. http://dx.doi.org/10.56093/ijans.v92i5.117167.

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Â Â The BayesianÂ model was applied for analyzing the first lactation in Karan Fries cattle. First lactation data of production (305-day or less milk yield and daily milk yield) were collected from the history-cum pedigree sheet and daily milk yield registers of the division of Dairy Cattle Breeding (DCB), National Dairy Research Institute (NDRI), Karnal. In the Bayesian paradigm, MCMC methods are applied to solve complex mathematical problems to estimate a large number of unknown parameters. Assuming linear mixed model and using the different prior set up, diagnostic of MCMC (Markov Chain Mon

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Christ, Theodore J., and Christopher David Desjardins. "Curriculum-Based Measurement of Reading: An Evaluation of Frequentist and Bayesian Methods to Model Progress Monitoring Data." Journal of Psychoeducational Assessment 36, no. 1 (2017): 55–73. http://dx.doi.org/10.1177/0734282917712174.

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Curriculum-Based Measurement of Oral Reading (CBM-R) is often used to monitor student progress and guide educational decisions. Ordinary least squares regression (OLSR) is the most widely used method to estimate the slope, or rate of improvement (ROI), even though published research demonstrates OLSR’s lack of validity and reliability, and imprecision of ROI estimates, especially after brief duration of monitoring (6-10 weeks). This study illustrates and examines the use of Bayesian methods to estimate ROI. Conditions included four progress monitoring durations (6, 8, 10, and 30 weeks), two sc

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Fässler, Sascha M. M., Andrew S. Brierley, and Paul G. Fernandes. "A Bayesian approach to estimating target strength." ICES Journal of Marine Science 66, no. 6 (2009): 1197–204. http://dx.doi.org/10.1093/icesjms/fsp008.

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Abstract Fässler, S. M. M., Brierley, A. S., and Fernandes, P. G. 2009. A Bayesian approach to estimating target strength. – ICES Journal of Marine Science, 66: 1197–1204. Currently, conventional models of target strength (TS) vs. fish length, based on empirical measurements, are used to estimate fish density from integrated acoustic data. These models estimate a mean TS, averaged over variables that modulate fish TS (tilt angle, physiology, and morphology); they do not include information about the uncertainty of the mean TS, which could be propagated through to estimates of fish abundance. W

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Alharbi, Yasser S., and Amr R. Kamel. "Fuzzy System Reliability Analysis for Kumaraswamy Distribution: Bayesian and Non-Bayesian Estimation with Simulation and an Application on Cancer Data Set." WSEAS TRANSACTIONS ON BIOLOGY AND BIOMEDICINE 19 (June 7, 2022): 118–39. http://dx.doi.org/10.37394/23208.2022.19.14.

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This paper proposes the fuzzy Bayesian (FB) estimation to get the best estimate of the unknown parameters of a two-parameter Kumaraswamy distribution from a frequentist point of view. These estimations of parameters are employed to estimate the fuzzy reliability function of the Kumaraswamy distribution and to select the best estimate of the parameters and fuzzy reliability function. To achieve this goal we investigate the efficiency of seven classical estimators and compare them with FB proposed estimation. Monte Carlo simulations and cancer data set applications are performed to compare the p

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Ambrose, Paul G., Jeffrey P. Hammel, Sujata M. Bhavnani, Christopher M. Rubino, Evelyn J. Ellis-Grosse, and George L. Drusano. "Frequentist and Bayesian Pharmacometric-Based Approaches To Facilitate Critically Needed New Antibiotic Development: Overcoming Lies, Damn Lies, and Statistics." Antimicrobial Agents and Chemotherapy 56, no. 3 (2011): 1466–70. http://dx.doi.org/10.1128/aac.01743-10.

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ABSTRACTAntimicrobial drug development has greatly diminished due to regulatory uncertainty about the magnitude of the antibiotic treatment effect. Herein we evaluate the utility of pharmacometric-based analyses for determining the magnitude of the treatment effect. Frequentist and Bayesian pharmacometric-based logistic regression analyses were conducted by using data from a phase 3 clinical trial of tigecycline-treated patients with hospital-acquired pneumonia (HAP) to evaluate relationships between the probability of microbiological or clinical success and the free-drug area under the concen

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Ben Zaabza, Hafedh, Abderrahmen Ben Gara, Hedi Hammami, Mohamed Amine Ferchichi, and Boulbaba Rekik. "Estimation of variance components of milk, fat, and protein yields of Tunisian Holstein dairy cattle using Bayesian and REML methods." Archives Animal Breeding 59, no. 2 (2016): 243–48. http://dx.doi.org/10.5194/aab-59-243-2016.

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Abstract. A multi-trait repeatability animal model under restricted maximum likelihood (REML) and Bayesian methods was used to estimate genetic parameters of milk, fat, and protein yields in Tunisian Holstein cows. The estimates of heritability for milk, fat, and protein yields from the REML procedure were 0.21 ± 0.05, 0.159 ± 0.04, and 0.158 ± 0.04, respectively. The corresponding results from the Bayesian procedure were 0.273 ± 0.02, 0.198 ± 0.01, and 0.187 ± 0.01. Heritability estimates tended to be larger via the Bayesian than those obtained by the REML method. Genetic and permanent enviro

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Chen, Ziqi, Cameron Fackler, and Ning Xiang. "Bayesian Parameter estimation of microphone positions, sound speed and dissipation for impedance tube measurements." INTER-NOISE and NOISE-CON Congress and Conference Proceedings 265, no. 7 (2023): 503–7. http://dx.doi.org/10.3397/in_2022_0070.

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With tube measurement widely used for acoustic measurements, calibration plays an important role in verifying and validating the measurement. This work applies a Bayesian method based on an air layer reflectance model to estimate the microphone positions, and sound speed in consideration of environmental effects on uncertainties of the normal incident impedance tube measurements. Bayesian theorem is applied to estimate the microphone positions and sound speed given the experimental data obtained from the transfer function method (TFM) in tube measurements. With a hypothetical air layer treated

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Dissertationen zum Thema "Bayesian estimate"

OLIVEIRA, ANA CRISTINA BERNARDO DE. "BAYESIAN MODEL TO ESTIMATE ADVERTISING RECALL IN MARKETING." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=7528@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO A importância de sistemas que monitorem continuamente as resposta dos consumidores à propaganda é notadamente reconhecida pela comunidade de pesquisa de mercado. A coleta sistemática deste tipo de informação é importante porque através desta, pode-se revisar campanhas anteriores, corrigir tendências detectadas em pré-testes e melhor orientar as tomadas de decisão nos setores de propaganda. O presente trabalho contém um modelo para tentar medir esta resposta baseada em Modelos Lineares Dinâmicos Generalizados. Analysis

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James, Peter Welbury. "Design and analysis of studies to estimate cerebral blood flow." Thesis, University of Newcastle Upon Tyne, 2002. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.251020.

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Rodewald, Oliver Russell. "Use of Bayesian inference to estimate diversion likelihood in a PUREX facility." Thesis, Massachusetts Institute of Technology, 2011. http://hdl.handle.net/1721.1/76951.

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Thesis (S.M. and S.B.)--Massachusetts Institute of Technology, Dept. of Nuclear Science and Engineering, 2011. Cataloged from PDF version of thesis. Includes bibliographical references (p. 66-67). Nuclear Fuel reprocessing is done today with the PUREX process, which has been demonstrated to work at industrial scales at several facilities around the world. Use of the PUREX process results in the creation of a stream of pure plutonium, which allows the process to be potentially used by a proliferator. Safeguards have been put in place by the IAEA and other agencies to guard against the

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SOUZA, MARCUS VINICIUS PEREIRA DE. "A BAYESIAN APPROACH TO ESTIMATE THE EFFICIENT OPERATIONAL COSTS OF ELECTRICAL ENERGY UTILITIES." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2008. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=12361@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO Esta tese apresenta os principais resultados de medidas de eficiência dos custos operacionais de 60 distribuidoras brasileiras de energia elétrica. Baseado no esquema yardstick competition, foi utilizado uma Rede Neural d e Kohonen (KNN) para identificar grupos de empresas similares. Os resultados obtidos pela KNN não são determinísticos, visto que os pesos sinápticos da rede são inicializados aleatoriamente. Então, é realizada uma simulação de Monte Carlo para encontrar os clusters mais frequentes. As medidas foram obtidas por mode

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Xiao, Yuqing. "Estimate the True Pass Probability for Near-Real-Time Monitor Challenge Data Using Bayesian Analysis." Digital Archive @ GSU, 2006. http://digitalarchive.gsu.edu/math_theses/20.

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The U.S. Army¡¯s Chemical Demilitarization are designed to store, treat and destroy the nation¡¯s aging chemical weapons. It operates Near-Real-Time Monitors and Deport Area Monitoring Systems to detect chemical agent at concentrations before they become dangerous to workers, public health and the environment. CDC recommends that the sampling and analytical methods measure within 25% of the true concentration 95% of the time, and if this criterion is not met the alarm set point or reportable level should be adjusted. Two methods were provided by Army¡¯s Programmatic Laboratory and Monitoring Q

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HUAMANI, LUIS ALBERTO NAVARRO. "A BAYESIAN PROCEDUCE TO ESTIMATE THE INDIVIDUAL CONTRIBUTION OF INDIVIDUAL END USES IN RESIDENCIAL ELECTRICAL ENERGY CONSUMPTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 1997. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=8691@1.

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CONSELHO NACIONAL DE DESENVOLVIMENTO CIENTÍFICO E TECNOLÓGICO Esta dissertação investiga a utilização do Modelo de Regressão Multivariada Seemingly Unrelated sob uma perspectiva Bayesiana, na estimação das curvas de carga dos principais eletrodomésticos. Será utilizada uma estrutura de Demanda Condicional (CDA), consideradas de especial interesse no setor comercial e residencial para o gerenciamento pelo lado da demanda (Demand Side Management) dos hábitos dos consumidores residenciais. O trabalho envolve três partes principais: uma apresentação das metodologias estatísticas cláss

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Bergström, David. "Bayesian optimization for selecting training and validation data for supervised machine learning : using Gaussian processes both to learn the relationship between sets of training data and model performance, and to estimate model performance over the entire problem domain." Thesis, Linköpings universitet, Artificiell intelligens och integrerade datorsystem, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:liu:diva-157327.

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Validation and verification in machine learning is an open problem which becomes increasingly important as its applications becomes more critical. Amongst the applications are autonomous vehicles and medical diagnostics. These systems all needs to be validated before being put into use or else the consequences might be fatal. This master’s thesis focuses on improving both learning and validating machine learning models in cases where data can either be generated or collected based on a chosen position. This can for example be taking and labeling photos at the position or running some simulatio

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Li, Qing. "Recurrent-Event Models for Change-Points Detection." Diss., Virginia Tech, 2015. http://hdl.handle.net/10919/78207.

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The driving risk of novice teenagers is the highest during the initial period after licensure but decreases rapidly. This dissertation develops recurrent-event change-point models to detect the time when driving risk decreases significantly for novice teenager drivers. The dissertation consists of three major parts: the first part applies recurrent-event change-point models with identical change-points for all subjects; the second part proposes models to allow change-points to vary among drivers by a hierarchical Bayesian finite mixture model; the third part develops a non-parametric Bayesian

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Benko, Matej. "Hledaní modelů pohybu a jejich parametrů pro identifikaci trajektorie cílů." Master's thesis, Vysoké učení technické v Brně. Fakulta strojního inženýrství, 2021. http://www.nusl.cz/ntk/nusl-445467.

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Táto práca sa zaoberá odstraňovaním šumu, ktorý vzniká z tzv. multilateračných meraní leteckých cieľov. Na tento účel bude využitá najmä teória Bayesovských odhadov. Odvodí sa aposteriórna hustota skutočnej (presnej) polohy lietadla. Spolu s polohou (alebo aj rýchlosťou) lietadla bude odhadovaná tiež geometria trajektórie lietadla, ktorú lietadlo v aktuálnom čase sleduje a tzv. procesný šum, ktorý charakterizuje ako moc sa skutočná trajektória môže od tejto líšiť. Odhad spomínaného procesného šumu je najdôležitejšou časťou tejto práce. Je odvodený prístup maximálnej vierohodnosti a Bayesovský

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Marković, Dimitrije, and Stefan J. Kiebel. "Comparative Analysis of Behavioral Models for Adaptive Learning in Changing Environments." Saechsische Landesbibliothek- Staats- und Universitaetsbibliothek Dresden, 2017. http://nbn-resolving.de/urn:nbn:de:bsz:14-qucosa-214867.

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Probabilistic models of decision making under various forms of uncertainty have been applied in recent years to numerous behavioral and model-based fMRI studies. These studies were highly successful in enabling a better understanding of behavior and delineating the functional properties of brain areas involved in decision making under uncertainty. However, as different studies considered different models of decision making under uncertainty, it is unclear which of these computational models provides the best account of the observed behavioral and neuroimaging data. This is an important issue,

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Bücher zum Thema "Bayesian estimate"

Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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Houston, Walter M. Empirical Bayes estimates of parameters from the logistic regression model. ACT, Inc., 1997.

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1972-, Raymer James, Willekens Frans, and University of Southampton. Division of Social Statistics., eds. International migration in Europe: Data, models and estimates. Wiley, 2008.

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Doppelhofer, Gernot. Determinants of long-term growth: A Bayesian averaging of classical estimates (BACE) approach. National Bureau of Economic Research, 2000.

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Tanabe, Kunio. BNDE, FORTRAN subroutines for computing Bayesian nonparametric univariate and bivariate density estimator. Institute of Statistical Mathematics, 1988.

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Torres, Maura Acevedo. Reduction of Uncertainty in Post-Event Seismic Loss Estimates Using Observation Data and Bayesian Updating. [publisher not identified], 2017.

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ha-meḥḳar, Banḳ Yiśraʼel Maḥleḳet, ed. The choice of a foreign price measure in a Bayesian estimated New-Keynesian model for Israel. Research Department, Bank of Israel, 2009.

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Walsh, Bruce, and Michael Lynch. Analysis of Short-term Selection Experiments: 2. Mixed-model and Bayesian Approaches. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198830870.003.0019.

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When the full pedigree of individuals whose values (records) were used in the selection decisions during an experiment (or breeding program) is known, LS analysis can be replaced by mixed models and their Bayesian extensions. In this setting, REML can be used to estimate genetic variances and BLUP can be used to estimate the mean breeding value in any given generation. The latter allows for genetic trends to be separated from environmental trends without the need for a control population. Under the infinitesimal model setting (wherein selection-induced allele-frequency changes are small during

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Quintana, José Mario, Carlos Carvalho, James Scott, and Thomas Costigliola. Extracting S&P500 and NASDAQ Volatility: The Credit Crisis of 2007–2008. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.13.

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This article demonstrates the utility of Bayesian modelling and inference in financial market volatility analysis, using the 2007-2008 credit crisis as a case study. It first describes the applied problem and goal of the Bayesian analysis before introducing the sequential estimation models. It then discusses the simulation-based methodology for inference, including Markov chain Monte Carlo (MCMC) and particle filtering methods for filtering and parameter learning. In the study, Bayesian sequential model choice techniques are used to estimate volatility and volatility dynamics for daily data fo

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Higdon, Dave, Katrin Heitmann, Charles Nakhleh, and Salman Habib. Combining simulations and physical observations to estimate cosmological parameters. Edited by Anthony O'Hagan and Mike West. Oxford University Press, 2018. http://dx.doi.org/10.1093/oxfordhb/9780198703174.013.26.

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This article focuses on the use of a Bayesian approach that combines simulations and physical observations to estimate cosmological parameters. It begins with an overview of the Λ-cold dark matter (CDM) model, the simplest cosmological model in agreement with the cosmic microwave background (CMB) and largescale structure analysis. The CDM model is determined by a small number of parameters which control the composition, expansion and fluctuations of the universe. The present study aims to learn about the values of these parameters using measurements from the Sloan Digital Sky Survey (SDSS). Co

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Buchteile zum Thema "Bayesian estimate"

Ghosh, J. K. "The Horvitz-Thompson Estimate and Basu’s Circus Revisited." In Bayesian Analysis in Statistics and Econometrics. Springer New York, 1992. http://dx.doi.org/10.1007/978-1-4612-2944-5_14.

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Pillonetto, Gianluigi, Tianshi Chen, Alessandro Chiuso, Giuseppe De Nicolao, and Lennart Ljung. "Bayesian Interpretation of Regularization." In Regularized System Identification. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-95860-2_4.

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AbstractIn the previous chapter, it has been shown that the regularization approach is particularly useful when information contained in the data is not sufficient to obtain a precise estimate of the unknown parameter vector and standard methods, such as least squares, yield poor solutions. The fact itself that an estimate is regarded as poor suggests the existence of some form of prior knowledge on the degree of acceptability of candidate solutions. It is this knowledge that guides the choice of the regularization penalty that is added as a corrective term to the usual sum of squared residual

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O’Hagan, Anthony, and Frank S. Wells. "Use of Prior Information to Estimate Costs in a Sewerage Operation." In Case Studies in Bayesian Statistics. Springer New York, 1993. http://dx.doi.org/10.1007/978-1-4612-2714-4_3.

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Weichert, Dorina, Elena Haedecke, Gunar Ernis, Sebastian Houben, Alexander Kister, and Stefan Wrobel. "Bayesian Inference for Fatigue Strength Estimation." In Cognitive Technologies. Springer Nature Switzerland, 2025. https://doi.org/10.1007/978-3-031-83097-6_6.

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Abstract A vital material property of metals is long life fatigue strength. It describes the maximum load that can be cyclically applied to a defined specimen for a number of cycles that is thought to represent an infinite lifetime. The experimental measurement of long life fatigue strength is costly, justifying the need to create a precise estimate with as few experiments as possible. We propose a new approach for estimating long life fatigue strength that defines a ready-to-use experimental and analysis procedure. It relies on probabilistic machine learning methods, efficiently connecting ex

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Henzinger, Thomas A., Mahyar Karimi, Konstantin Kueffner, and Kaushik Mallik. "Monitoring Algorithmic Fairness." In Computer Aided Verification. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-37703-7_17.

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AbstractMachine-learned systems are in widespread use for making decisions about humans, and it is important that they are fair, i.e., not biased against individuals based on sensitive attributes. We present runtime verification of algorithmic fairness for systems whose models are unknown, but are assumed to have a Markov chain structure. We introduce a specification language that can model many common algorithmic fairness properties, such as demographic parity, equal opportunity, and social burden. We build monitors that observe a long sequence of events as generated by a given system, and ou

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Peña, José M., Víctor Robles, Óscar Marbán, and María S. Pérez. "Bayesian Methods to Estimate Future Load in Web Farms." In Advances in Web Intelligence. Springer Berlin Heidelberg, 2004. http://dx.doi.org/10.1007/978-3-540-24681-7_24.

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Evensen, Geir, Femke C. Vossepoel, and Peter Jan van Leeuwen. "Maximum a Posteriori Solution." In Springer Textbooks in Earth Sciences, Geography and Environment. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-96709-3_3.

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AbstractWe will now introduce a fundamental approximation used in most practical data-assimilation methods, namely the definition of Gaussian priors. This approximation simplifies the Bayesian posterior, which allows us to compute the maximum a posteriori (MAP) estimate and sample from the posterior pdf. This chapter will introduce the Gaussian approximation and then discuss the Gauss–Newton method for finding the MAP estimate. This method is the starting point for many of the data-assimilation algorithms discussed in the following chapters.

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Wickramasuriya, Dilranjan S., and Rose T. Faghih. "Introduction." In Bayesian Filter Design for Computational Medicine. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-47104-9_1.

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AbstractThe human body is an intricate network of multiple functioning sub-systems. Many unobserved processes quietly keep running within the body even while we remain largely unconscious of them. For decades, scientists have sought to understand how different physiological systems work and how they can be mathematically modeled. Mathematical models of biological systems provide key scientific insights and also help guide the development of technologies for treating disorders when proper functioning no longer occurs. One of the challenges encountered with physiological systems is that, in a nu

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Joanes, Derrick N., Christine A. Gill, and Andrew J. Baczkowski. "Simulation of a Bayesian Interval Estimate for a Heterogeneity Measure." In Compstat. Physica-Verlag HD, 1994. http://dx.doi.org/10.1007/978-3-642-52463-9_20.

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Mira, Antonietta, and Paolo Tenconi. "Bayesian Estimate of Default Probabilities via MCMC with Delayed Rejection." In Seminar on Stochastic Analysis, Random Fields and Applications IV. Birkhäuser Basel, 2004. http://dx.doi.org/10.1007/978-3-0348-7943-9_17.

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Konferenzberichte zum Thema "Bayesian estimate"

Mertenskötter, Lutz, and Markus Kantner. "Bayesian Estimation of Frequency Noise in Narrow-Linewidth Lasers." In CLEO: Applications and Technology. Optica Publishing Group, 2024. http://dx.doi.org/10.1364/cleo_at.2024.jtu2a.45.

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We present a statistical inference approach to estimate the frequency noise power spectral density of narrow-linewidth lasers from delayed self-heterodyne beat note experiments in the presence of considerable measurement noise.

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Scharzenberger, Cody, Shay Snyder, Sumedh R. Risbud, Joe Hays, and Maryam Parsa. "Learning to Estimate Regions of Attraction of Autonomous Dynamical Systems Using Bayesian Physics Informed Neural Networks." In 2024 International Conference on Neuromorphic Systems (ICONS). IEEE, 2024. https://doi.org/10.1109/icons62911.2024.00036.

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a, Zeyu, and Zheyu Ji a. "A Novel Bayesian Framework for Inverse Problems in Precision Agriculture." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.113662.

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An essential problem in precision agriculture is to accurately model and predict root-zone (top 1 m of soil) soil moisture profile given soil properties and precipitation and evapotranspiration information. This is typically achieved by solving agro-hydrological models. Nowadays, most of these models are based on the standard Richards equation (RE), a highly nonlinear, degenerate elliptic-parabolic partial differential equation that describes irrigation, precipitation, evapotranspiration, runoff, and drainage through soils. Recently, the standard RE has been generalized to time-fractional RE w

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Patel, Ishan, and Gheorghe Bota. "Mechanistic Model as a Bias to Machine Learning Algorithm for Confident Prediction of Corrosion." In CONFERENCE 2023. AMPP, 2023. https://doi.org/10.5006/c2023-19108.

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Abstract Bayesian network is employed to estimate a risk-based life cycle cost of corrosion for assets. It has been highly recognized that inclusion of mechanistic models to a Bayesian network can increase the confidence in estimation of corrosion rates. However, coefficients of mechanistic models are often unknown, especially when complex rate processes are involved, which discourages the usage of the model. A methodology is proposed here, to introduce a mechanistic model as a bias to a regressive machine learning (ML) algorithm. No attempts have been made to obtain phenomenological coefficie

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de Melo, Brian A. R., Raony C. C. Cesar, and Carlos A. B. Pereira. "Sample sizes to estimate proportions and correlation." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759606.

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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

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Gu, L., G. Li, J. Abramczyk, and J. Prybylski. "A Bayesian Estimate of Vehicle Safety Performance." In SAE 2005 World Congress & Exhibition. SAE International, 2005. http://dx.doi.org/10.4271/2005-01-0822.

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Torres-Avilés, F., C. Molina, and M. J. Muñoz. "Bayesian approaches for Poisson models to estimate bivariate relative risks." In XI BRAZILIAN MEETING ON BAYESIAN STATISTICS: EBEB 2012. AIP, 2012. http://dx.doi.org/10.1063/1.4759618.

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Dan, Zhiping, Xi Chen, Haitao Gan, and Changxin Gao. "Locally Adaptive Shearlet Denoising Based on Bayesian MAP Estimate." In Graphics (ICIG). IEEE, 2011. http://dx.doi.org/10.1109/icig.2011.134.

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Nagel, Joseph B., and Bruno Sudret. "A Bayesian Multilevel Approach to Optimally Estimate Material Properties." In Second International Conference on Vulnerability and Risk Analysis and Management (ICVRAM) and the Sixth International Symposium on Uncertainty, Modeling, and Analysis (ISUMA). American Society of Civil Engineers, 2014. http://dx.doi.org/10.1061/9780784413609.151.

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Berichte der Organisationen zum Thema "Bayesian estimate"

Mulcahy, Garrett, Dusty Brooks, and Brian Ehrhart. Using Bayesian Methodology to Estimate Liquefied Natural Gas Leak Frequencies. Office of Scientific and Technical Information (OSTI), 2021. http://dx.doi.org/10.2172/1782412.

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Read, Matthew. Estimating the Effects of Monetary Policy in Australia Using Sign-restricted Structural Vector Autoregressions. Reserve Bank of Australia, 2023. http://dx.doi.org/10.47688/rdp2022-09.

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Existing estimates of the macroeconomic effects of Australian monetary policy tend to be based on strong, potentially contentious, assumptions. I estimate these effects under weaker assumptions. Specifically, I estimate a structural vector autoregression identified using a variety of sign restrictions, including restrictions on impulse responses to a monetary policy shock, the monetary policy reaction function, and the relationship between the monetary policy shock and a proxy for this shock. I use an approach to Bayesian inference that accounts for the problem of posterior sensitivity to the

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Granados, Camilo, and Daniel Parra-Amado. Output Gap Measurement after COVID for Colombia: Lessons from a Permanent-Transitory Approach. Banco de la República, 2025. https://doi.org/10.32468/be.1295.

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We estimate the output gap for the Colombian economy explicitly accounting for the COVID-19 period. Our estimates reveal a significant $20$\% decline in the output gap but with a faster recovery compared to previous crises. Our empirical strategy follows a two-stage Bayesian vector autoregressive (BSVAR) model where i) a scaling factor in the reduced form of VAR is used to model extreme data, such as those observed around the COVID-19 period, and ii) permanent and transitory shocks are structurally identified. As a result, we obtain that a single structural shock explains the potential GDP, wh

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López, Lucía, Florens Odendahl, Susana Párraga, and Edgar Silgado-Gómez. The pass-through to inflation of gas price shocks. Banco de España, 2025. https://doi.org/10.53479/39118.

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This paper uses a Bayesian Structural Vector Autoregressive (BSVAR) framework to estimate the pass-through of unexpected gas price supply shocks to HICP inflation in the euro area and its four largest economies. Compared with oil price shocks, gas price shocks have an approximately one-third smaller pass-through to headline inflation. Country-specific results indicate that gas price increases matter more for German, Spanish and Italian inflation than for French inflation, hinging on the reliance on energy commodities in consumption, production and different electricity price regulations. Consi

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Galindo, Arturo, and Victoria Nuguer. Fuel-Price Shocks and Inflation in Latin America and the Caribbean. Inter-American Development Bank, 2023. http://dx.doi.org/10.18235/0004724.

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We estimate the impact of fuel-commodity price shocks on inflation and inflation expectations for eight Latin American countries in which monetary policy follows inflation-targeting frameworks. We use Bayesian Vector Autoregressive models (BVARs) and data from 2005 and up to 2022 to quantify these impacts. We find that the fuel-price shocks are significant in all cases and the response ranges between 0.01 and 0.04 percentage points of inflation, following a 1 p.p. shock to fuel prices. A variance decomposition exercise shows that more than 50% of the outburst in inflation that these countries

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Albis, Manuel Leonard, Mara Claire Tayag, and Jong Woo Kang. Estimating Regional Integration Using the Bayesian State-Space Approach. Asian Development Bank, 2024. http://dx.doi.org/10.22617/wps230622-2.

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Estimating regional integration faces challenges due to incomplete data. This paper addresses this through the dynamic factor model estimated using the Bayesian state-space approach. Bilateral economic integration (BEI) indexes are estimated across four dimensions: trade, foreign direct investments, finance, and migration. The regional integration index (RII) of Asia and the Pacific is calculated by applying network density to the BEI estimates. The RII declined slightly in recent years, with the network centering more around the People’s Republic of China.

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BARUMERLI, Roberto, and Piotr MAJDAK. Towards a general probabilistic framework to predict human sound localization. Verlag der Österreichischen Akademie der Wissenschaften, 2025. https://doi.org/10.1553/ica_2022_probabilistic-framework.

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Our hearing system plays a significant role in understanding the space around us. Such a process is the result of the dynamic interaction between the listener and the environment. Yet, predicting how our hearing system drives space perception is still an open problem. In this work, we explore the possibility of employing Bayesian models as a quantitative method to predict human behavior in acoustic environments. We describe how perceptually relevant features can be used to estimate spatial quantities from the acoustic space (e.g., directional sound location). We further describe how the spatia

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Gungor, Osman, Imad Al-Qadi, and Navneet Garg. Pavement Data Analytics for Collected Sensor Data. Illinois Center for Transportation, 2021. http://dx.doi.org/10.36501/0197-9191/21-034.

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The Federal Aviation Administration instrumented four concrete slabs of a taxiway at the John F. Kennedy International Airport to collect pavement responses under aircraft and environmental loading. The study started with developing preprocessing scripts to organize, structure, and clean the collected data. As a result of the preprocessing step, the data became easier and more intuitive for pavement engineers and researchers to transform and process. After the data were cleaned and organized, they were used to develop two prediction models. The first prediction model employs a Bayesian calibra

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Cheng, Benny N., and Lap S. Tam. Bayesian Missile System Reliability from Point Estimates. Defense Technical Information Center, 2014. http://dx.doi.org/10.21236/ada611099.

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Kurozumi, Takushi, Ryohei Oishi, and Willem Van Zandweghe. Sticky Information Versus Sticky Prices Revisited: A Bayesian VAR-GMM Approach. Federal Reserve Bank of Cleveland, 2022. http://dx.doi.org/10.26509/frbc-wp-202234.

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Several Phillips curves based on sticky information and sticky prices are estimated and compared using Bayesian VAR-GMM. This method derives expectations in each Phillips curve from a VAR and estimates the Phillips curve parameters and the VAR coefficients simultaneously. Quasi-marginal likelihood-based model comparison selects a dual stickiness Phillips curve in which, each period, some prices remain unchanged, consistent with micro evidence. Moreover, sticky information is a more plausible source of inflation inertia in the Phillips curve than other sources proposed in previous studies. Stic

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