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Thematische Bibliographien / Bayesian estimate / Zeitschriftenartikel

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Zeitschriftenartikel zum Thema „Bayesian estimate“

Autor: Grafiati

Veröffentlicht am 28. Juni 2021

Zuletzt aktualisiert am 29. Juli 2025

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1

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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Dhiraj Kumar Singh and Himanshu Bhatt. "Estimates of some entropies and their relevance in financial market." Gulf Journal of Mathematics 20 (June 14, 2025): 222–41. https://doi.org/10.56947/gjom.v20i.2820.

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Using various types of bivariate beta priors such as bivariate beta type-I prior, Conner and Mosimann bivariate beta prior and bivariate beta type-III prior, the Bayesian estimate of the entropy of type-(α) and entropy of type-(α,β) are derived for multinomial likelihood. Bayesian estimates of Shannon entropy for different bivariate beta priors can be represented as a limiting function of Bayesian estimates of entropy of type-(α). In this paper, the parameters of the bivariate beta type-III prior under multinomial likelihood in Moody's corporate bond default rates data are selected using the B

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Okasha, Hassan, Yuhlong Lio, and Mohammed Albassam. "On Reliability Estimation of Lomax Distribution under Adaptive Type-I Progressive Hybrid Censoring Scheme." Mathematics 9, no. 22 (2021): 2903. http://dx.doi.org/10.3390/math9222903.

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Bayesian estimates involve the selection of hyper-parameters in the prior distribution. To deal with this issue, the empirical Bayesian and E-Bayesian estimates may be used to overcome this problem. The first one uses the maximum likelihood estimate (MLE) procedure to decide the hyper-parameters; while the second one uses the expectation of the Bayesian estimate taken over the joint prior distribution of the hyper-parameters. This study focuses on establishing the E-Bayesian estimates for the Lomax distribution shape parameter functions by utilizing the Gamma prior of the unknown shape paramet

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AGBAJE, Olorunsola F., Stephen D. LUZIO, Ahmed I. S. ALBARRAK, David J. LUNN, David R. OWENS, and Roman HOVORKA. "Bayesian hierarchical approach to estimate insulin sensitivity by minimal model." Clinical Science 105, no. 5 (2003): 551–60. http://dx.doi.org/10.1042/cs20030117.

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We adopted Bayesian analysis in combination with hierarchical (population) modelling to estimate simultaneously population and individual insulin sensitivity (SI) and glucose effectiveness (SG) with the minimal model of glucose kinetics using data collected during insulin-modified intravenous glucose tolerance test (IVGTT) and made comparison with the standard non-linear regression analysis. After fasting overnight, subjects with newly presenting Type II diabetes according to World Health Organization criteria (n=65; 53 males, 12 females; age, 54±9 years; body mass index, 30.4±5.2 kg/m2; means

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MOLINARES, CARLOS A., and CHRIS P. TSOKOS. "BAYESIAN RELIABILITY APPROACH TO THE POWER LAW PROCESS WITH SENSITIVITY ANALYSIS TO PRIOR SELECTION." International Journal of Reliability, Quality and Safety Engineering 20, no. 01 (2013): 1350004. http://dx.doi.org/10.1142/s0218539313500046.

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The intensity function is the key entity to the power law process, also known as the Weibull process or nonhomogeneous Poisson process. It gives the rate of change of the reliability of a system as a function of time. We illustrate that a Bayesian analysis is applicable to the power law process through the intensity function. First, we show using real data, that one of the two parameters in the intensity function behaves as a random variable. With a sequence of estimates of the subject parameter we proceeded to identify the probability distribution that characterizes its behavior. Using the co

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Zhang, Qingyang, and Xuan Shi. "A mixture copula Bayesian network model for multimodal genomic data." Cancer Informatics 16 (January 1, 2017): 117693511770238. http://dx.doi.org/10.1177/1176935117702389.

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Gaussian Bayesian networks have become a widely used framework to estimate directed associations between joint Gaussian variables, where the network structure encodes the decomposition of multivariate normal density into local terms. However, the resulting estimates can be inaccurate when the normality assumption is moderately or severely violated, making it unsuitable for dealing with recent genomic data such as the Cancer Genome Atlas data. In the present paper, we propose a mixture copula Bayesian network model which provides great flexibility in modeling non-Gaussian and multimodal data fo

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Richard, Michael D., and Richard P. Lippmann. "Neural Network Classifiers Estimate Bayesian a posteriori Probabilities." Neural Computation 3, no. 4 (1991): 461–83. http://dx.doi.org/10.1162/neco.1991.3.4.461.

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Many neural network classifiers provide outputs which estimate Bayesian a posteriori probabilities. When the estimation is accurate, network outputs can be treated as probabilities and sum to one. Simple proofs show that Bayesian probabilities are estimated when desired network outputs are 1 of M (one output unity, all others zero) and a squared-error or cross-entropy cost function is used. Results of Monte Carlo simulations performed using multilayer perceptron (MLP) networks trained with backpropagation, radial basis function (RBF) networks, and high-order polynomial networks graphically dem

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Tansitpong, Praowpan. "Probabilistic Model of Patient Classification Using Bayesian Model." International Journal of Reliable and Quality E-Healthcare 13, no. 1 (2024): 1–19. http://dx.doi.org/10.4018/ijrqeh.348579.

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The research emphasizes the effectiveness of Bayesian classification algorithms in predicting patient visits in healthcare settings. Bayesian algorithms examine past patient data to detect intricate patterns in admission dynamics, including demographic, clinical, and temporal factors. Through the use of Bayesian principles, prediction models are able to estimate the probability of certain patient demographics occurring at certain intervals, therefore assisting in the allocation of resources and the management of operations. Probabilities that have been estimated are used to make choices on sta

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Wei, Cheng Dong, Fu Wang, and Huan Qi Wei. "Bayesian Estimate of Exponential Parameter with Missing Data." Applied Mechanics and Materials 321-324 (June 2013): 904–8. http://dx.doi.org/10.4028/www.scientific.net/amm.321-324.904.

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We discuss the empirical Bayesian estimation and the noninformative prior Bayesian estimation of Exponential parameter in the missing data occasion. By setting different prior distributions, we get different bayesian risks and compare the numerical simulation results through the MATLAB programming.

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Al-Hossain, Abdullah Y. "Burr-X Model Estimate using Bayesian and non-Bayesian Approaches." Journal of Mathematics and Statistics 12, no. 2 (2016): 77–85. http://dx.doi.org/10.3844/jmssp.2016.77.85.

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Lehky, Sidney R. "Bayesian Estimation of Stimulus Responses in Poisson Spike Trains." Neural Computation 16, no. 7 (2004): 1325–43. http://dx.doi.org/10.1162/089976604323057407.

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A Bayesian method is developed for estimating neural responses to stimuli, using likelihood functions incorporating the assumption that spike trains follow either pure Poisson statistics or Poisson statistics with a refractory period. The Bayesian and standard estimates of the mean and variance of responses are similar and asymptotically converge as the size of the data sample increases. However, the Bayesian estimate of the variance of the variance is much lower. This allows the Bayesian method to provide more precise interval estimates of responses. Sensitivity of the Bayesian method to the

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Lau, John W., Tak Kuen Siu, and Hailiang Yang. "On Bayesian Mixture Credibility." ASTIN Bulletin 36, no. 02 (2006): 573–88. http://dx.doi.org/10.2143/ast.36.2.2017934.

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We introduce a class of Bayesian infinite mixture models first introduced by Lo (1984) to determine the credibility premium for a non-homogeneous insurance portfolio. The Bayesian infinite mixture models provide us with much flexibility in the specification of the claim distribution. We employ the sampling scheme based on a weighted Chinese restaurant process introduced in Lo et al. (1996) to estimate a Bayesian infinite mixture model from the claim data. The Bayesian sampling scheme also provides a systematic way to cluster the claim data. This can provide some insights into the risk characte

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Lau, John W., Tak Kuen Siu, and Hailiang Yang. "On Bayesian Mixture Credibility." ASTIN Bulletin 36, no. 2 (2006): 573–88. http://dx.doi.org/10.1017/s0515036100014677.

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We introduce a class of Bayesian infinite mixture models first introduced by Lo (1984) to determine the credibility premium for a non-homogeneous insurance portfolio. The Bayesian infinite mixture models provide us with much flexibility in the specification of the claim distribution. We employ the sampling scheme based on a weighted Chinese restaurant process introduced in Lo et al. (1996) to estimate a Bayesian infinite mixture model from the claim data. The Bayesian sampling scheme also provides a systematic way to cluster the claim data. This can provide some insights into the risk characte

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Rubilar-Torrealba, Rolando, Karime Chahuán-Jiménez, Hanns de la Fuente-Mella, and Claudio Elórtegui-Gómez. "Bayesian Approach to Stochastic Estimation of Population Survival Curves in Chile Using ABC Techniques and Its Impact over Social Structures." Computation 12, no. 8 (2024): 154. http://dx.doi.org/10.3390/computation12080154.

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In Chile and worldwide, life expectancy has consistently increased over the past six decades. Thus, the purpose of this study was to identify, measure, and estimate the population mortality ratios in Chile, mortality estimates are used to calculate life expectancy when constructing life tables. The Bayesian approach, specifically through Approximate Bayesian Computation (ABC) is employed to optimize parameter selection for these calculations. ABC corresponds to a class of computational methods rooted in Bayesian statistics that could be used to estimate the posterior distributions of the model

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Emelyanov, V. E., and S. P. Matyuk. "BAYESIAN ESTIMATE OF TELECOMMUNICATION SYSTEMS PREPAREDNESS." Civil Aviation High Technologies 24, no. 1 (2021): 16–22. http://dx.doi.org/10.26467/2079-0619-2021-24-1-16-22.

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Phang, Sen, Pietro Ravani, Jeffrey Schaefer, Bruce Wright, and Kevin Mclaughlin. "Internal Medicine residents use heuristics to estimate disease probability." Canadian Medical Education Journal 6, no. 2 (2015): e71-e77. http://dx.doi.org/10.36834/cmej.36653.

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Background: Training in Bayesian reasoning may have limited impact on accuracy of probability estimates. In this study, our goal was to explore whether residents previously exposed to Bayesian reasoning use heuristics rather than Bayesian reasoning to estimate disease probabilities. We predicted that if residents use heuristics then post-test probability estimates would be increased by non-discriminating clinical features or a high anchor for a target condition.Method: We randomized 55 Internal Medicine residents to different versions of four clinical vignettes and asked them to estimate proba

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Okasha, Hassan M., Heba S. Mohammed, and Yuhlong Lio. "E-Bayesian Estimation of Reliability Characteristics of a Weibull Distribution with Applications." Mathematics 9, no. 11 (2021): 1261. http://dx.doi.org/10.3390/math9111261.

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Given a progressively type-II censored sample, the E-Bayesian estimates, which are the expected Bayesian estimates over the joint prior distributions of the hyper-parameters in the gamma prior distribution of the unknown Weibull rate parameter, are developed for any given function of unknown rate parameter under the square error loss function. In order to study the impact from the selection of hyper-parameters for the prior, three different joint priors of the hyper-parameters are utilized to establish the theoretical properties of the E-Bayesian estimators for four functions of the rate param

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Cu Thi, Phuong, James Ball, and Ngoc Dao. "Uncertainty Estimation Using the Glue and Bayesian Approaches in Flood Estimation: A case Study—Ba River, Vietnam." Water 10, no. 11 (2018): 1641. http://dx.doi.org/10.3390/w10111641.

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In the last few decades tremendous progress has been made in the use of catchment models for the analysis and understanding of hydrologic systems. A common application involves the use of these models to predict flows at catchment outputs. However, the outputs predicted by these models are often deterministic because they focused only on the most probable forecast without an explicit estimate of the associated uncertainty. This paper uses Bayesian and Generalized Likelihood Uncertainty Estimation (GLUE) approaches to estimate uncertainty in catchment modelling parameter values and uncertainty

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Chen, Ziqi, Kirill V. Horoshenkov, and Ning Xiang. "Bayesian inference for boundary admittance estimation using a multipole model for room-acoustic simulation." Journal of the Acoustical Society of America 150, no. 4 (2021): A348. http://dx.doi.org/10.1121/10.0008540.

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Acoustic surface admittance/impedance at room boundaries is essential for wave-based room-acoustic simulations. In this work, two levels of Bayesian inference are applied to estimate the surface admittance based on a multipole admittance model. This work estimates the order of the multipole admittance model through the high level of inference, Bayesian model selection. The first (low) level of inference, Bayesian parameter estimation, is applied to estimate the parameter values of the surface admittance model once model order is selected. This work approximates the frequency-dependent admittan

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Vilar, M. J., J. Ranta, S. Virtanen, and H. Korkeala. "Bayesian Estimation of the True Prevalence and of the Diagnostic Test Sensitivity and Specificity of EnteropathogenicYersiniain Finnish Pig Serum Samples." BioMed Research International 2015 (2015): 1–7. http://dx.doi.org/10.1155/2015/931542.

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Bayesian analysis was used to estimate the pig’s and herd’s true prevalence of enteropathogenicYersiniain serum samples collected from Finnish pig farms. The sensitivity and specificity of the diagnostic test were also estimated for the commercially available ELISA which is used for antibody detection against enteropathogenicYersinia. The Bayesian analysis was performed in two steps; the first step estimated the prior true prevalence of enteropathogenicYersiniawith data obtained from a systematic review of the literature. In the second step, data of the apparent prevalence (cross-sectional stu

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Rabie, Abdalla, and Junping Li. "E-Bayesian Estimation Based on Burr-X Generalized Type-II Hybrid Censored Data." Symmetry 11, no. 5 (2019): 626. http://dx.doi.org/10.3390/sym11050626.

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In this article, we are concerned with the E-Bayesian (the expectation of Bayesian estimate) method, the maximum likelihood and the Bayesian estimation methods of the shape parameter, and the reliability function of one-parameter Burr-X distribution. A hybrid generalized Type-II censored sample from one-parameter Burr-X distribution is considered. The Bayesian and E-Bayesian approaches are studied under squared error and LINEX loss functions by using the Markov chain Monte Carlo method. Confidence intervals for maximum likelihood estimates, as well as credible intervals for the E-Bayesian and

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Muharisa, Catrin, Ferra Yanuar, and Dodi Devianto. "Simulation Study The Using of Bayesian Quantile Regression in Nonnormal Error." CAUCHY 5, no. 3 (2018): 121. http://dx.doi.org/10.18860/ca.v5i3.5633.

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The purposes of this paper is to introduce the ability of the Bayesian quantile regression method in overcoming the problem of the nonnormal errors using asymmetric laplace distribution on simulation study. <strong>Method: </strong>We generate data and set distribution of error is asymmetric laplace distribution error, which is non normal data. In this research, we solve the nonnormal problem using quantile regression method and Bayesian quantile regression method and then we compare. The approach of the quantile regression is to separate or divide the data into any quantiles, esti

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Chen, Lu, Daping Bi, and Jifei Pan. "Two-Dimensional Angle Estimation of Two-Parallel Nested Arrays Based on Sparse Bayesian Estimation." Sensors 18, no. 10 (2018): 3553. http://dx.doi.org/10.3390/s18103553.

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To increase the number of estimable signal sources, two-parallel nested arrays are proposed, which consist of two subarrays with sensors, and can estimate the two-dimensional (2-D) direction of arrival (DOA) of signal sources. To solve the problem of direction finding with two-parallel nested arrays, a 2-D DOA estimation algorithm based on sparse Bayesian estimation is proposed. Through a vectorization matrix, smoothing reconstruction matrix and singular value decomposition (SVD), the algorithm reduces the size of the sparse dictionary and data noise. A sparse Bayesian learning algorithm is us

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Staggs, Vincent S., and Byron J. Gajewski. "Bayesian and frequentist approaches to assessing reliability and precision of health-care provider quality measures." Statistical Methods in Medical Research 26, no. 3 (2015): 1341–49. http://dx.doi.org/10.1177/0962280215577410.

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Our purpose was to compare frequentist, empirical Bayes, and Bayesian hierarchical model approaches to estimating reliability of health care quality measures, including construction of credible intervals to quantify uncertainty in reliability estimates, using data on inpatient fall rates on hospital nursing units. Precision of reliability estimates and Bayesian approaches to estimating reliability are not well studied. We analyzed falls data from 2372 medical units; the rate of unassisted falls per 1000 inpatient days was the measure of interest. The Bayesian methods “shrunk” the observed fall

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Caraiani, P. "Bayesian estimation of the Okun coefficient for Romania." Acta Oeconomica 60, no. 1 (2010): 79–92. http://dx.doi.org/10.1556/aoecon.60.2010.1.5.

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In this paper I use a New Keynesian model with unemployment and estimate it for the Romanian economy using Bayesian techniques. I use the estimated model to derive an estimation of the Okun coefficient. I alternatively estimate the Okun coefficient using the Bayesian linear regression. The results show that the Okun coefficient is high in the Romanian economy implying that the current crisis will have a severe impact on the labour market as well as important social effects.

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Thorson, James T., and Jim Berkson. "Multispecies estimation of Bayesian priors for catchability trends and density dependence in the US Gulf of Mexico." Canadian Journal of Fisheries and Aquatic Sciences 67, no. 6 (2010): 936–54. http://dx.doi.org/10.1139/f10-040.

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Fishery-dependent catch-per-unit-effort (CPUE) derived indices of stock abundance are commonly used in fishery stock assessment models and may be significantly biased due to changes in catchability over time. Factors causing time-varying catchability include density-dependent habitat selection and technology improvements such as global positioning systems. In this study, we develop a novel multispecies method to estimate Bayesian priors for catchability functional parameters. This method uses the deviance information criterion to select a parsimonious functional model for catchability among 10

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Dutta, Subhankar, Hana N. Alqifari, and Amani Almohaimeed. "Bayesian and non-bayesian inference for logistic-exponential distribution using improved adaptive type-II progressively censored data." PLOS ONE 19, no. 5 (2024): e0298638. http://dx.doi.org/10.1371/journal.pone.0298638.

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Improved adaptive type-II progressive censoring schemes (IAT-II PCS) are increasingly being used to estimate parameters and reliability characteristics of lifetime distributions, leading to more accurate and reliable estimates. The logistic exponential distribution (LED), a flexible distribution with five hazard rate forms, is employed in several fields, including lifetime, financial, and environmental data. This research aims to enhance the accuracy and reliability estimation capabilities for the logistic exponential distribution under IAT-II PCS. By developing novel statistical inference met

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Wang, Chenlan, Chongjie Zhang, and X. Jessie Yang. "Automation reliability and trust: A Bayesian inference approach." Proceedings of the Human Factors and Ergonomics Society Annual Meeting 62, no. 1 (2018): 202–6. http://dx.doi.org/10.1177/1541931218621048.

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Research shows that over repeated interactions with automation, human operators are able to learn how reliable the automation is and update their trust in automation. The goal of the present study is to investigate if this learning and inference process approximately follow the principle of Bayesian probabilistic inference. First, we applied Bayesian inference to estimate human operators’ perceived system reliability and found high correlations between the Bayesian estimates and the perceived reliability for the majority of the participants. We then correlated the Bayesian estimates with human

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Vallejo, Benjamin, Jr, and Alexander Aloy. "Estimating the rarity of birds and its ecological context in the University of the Philippines Diliman Campus." SciEnggJ 1, no. 1 (2008): 32–36. http://dx.doi.org/10.54645/ipyz58175.

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In this paper we use frequentist (classical) and Bayesian inference to estimate the rarity of birds in the University of the Philippines Diliman campus. Rare species have a detection probability of 1%. As sightings of certain species of birds are extremely rare, a frequentist approach to estimation will often result in overestimates of observation precision. Using McArdle’s rarity and Bayesian inference we estimated the probability of detecting rare species as between 4 to 10%. The Bayesian estimates are lower and may be a better method for estimating rarity. The coefficient of variation (CV)

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McCann, Brian T. "Using Bayesian Updating to Improve Decisions under Uncertainty." California Management Review 63, no. 1 (2020): 26–40. http://dx.doi.org/10.1177/0008125620948264.

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Decision making requires managers to constantly estimate the probability of uncertain outcomes and update those estimates in light of new information. This article provides guidance to managers on how they can improve that process by more explicitly adopting a Bayesian approach. Clear understanding and application of the Bayesian approach leads to more accurate probability estimates, resulting in better informed decisions. More importantly, adopting a Bayesian approach, even informally, promises to improve the quality of managerial thinking, analysis, and decisions in a variety of additional w

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Nagy, M., M. H. Abu-Moussa, Adel Fahad Alrasheedi, and A. Rabie. "Expected Bayesian estimation for exponential model based on simple step stress with Type-I hybrid censored data." Mathematical Biosciences and Engineering 19, no. 10 (2022): 9773–91. http://dx.doi.org/10.3934/mbe.2022455.

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<abstract><p>The procedure of selecting the values of hyper-parameters for prior distributions in Bayesian estimate has produced many problems and has drawn the attention of many authors, therefore the expected Bayesian (E-Bayesian) estimation method to overcome these problems. These approaches are used based on the step-stress acceleration model under the Exponential Type-I hybrid censored data in this study. The values of the distribution parameters are derived. To compare the E-Bayesian estimates to the other estimates, a comparative study was conducted using the simulation rese

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Oladyshkin, Sergey, and Wolfgang Nowak. "The Connection between Bayesian Inference and Information Theory for Model Selection, Information Gain and Experimental Design." Entropy 21, no. 11 (2019): 1081. http://dx.doi.org/10.3390/e21111081.

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We show a link between Bayesian inference and information theory that is useful for model selection, assessment of information entropy and experimental design. We align Bayesian model evidence (BME) with relative entropy and cross entropy in order to simplify computations using prior-based (Monte Carlo) or posterior-based (Markov chain Monte Carlo) BME estimates. On the one hand, we demonstrate how Bayesian model selection can profit from information theory to estimate BME values via posterior-based techniques. Hence, we use various assumptions including relations to several information criter

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Jackman, Simon. "Estimation and Inference Are Missing Data Problems: Unifying Social Science Statistics via Bayesian Simulation." Political Analysis 8, no. 4 (2000): 307–32. http://dx.doi.org/10.1093/oxfordjournals.pan.a029818.

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Bayesian simulation is increasingly exploited in the social sciences for estimation and inference of model parameters. But an especially useful (if often overlooked) feature of Bayesian simulation is that it can be used to estimate any function of model parameters, including “auxiliary” quantities such as goodness-of-fit statistics, predicted values, and residuals. Bayesian simulation treats these quantities as if they were missing data, sampling from their implied posterior densities. Exploiting this principle also lets researchers estimate models via Bayesian simulation where maximum-likelih

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Jamnia, Abdul Rashid, Ahmad Ali Keikha, Mahmoud Ahmadpour, Abdoul Ahad Cissé, and Mohammad Rokouei. "Applying bayesian population assessment models to artisanal, multispecies fisheries in the Northern Mokran Sea, Iran." Nature Conservation 28 (August 13, 2018): 61–89. http://dx.doi.org/10.3897/natureconservation.28.25212.

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Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian

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Jamnia, Abdul Rashid, Ahmad Ali Keikha, Mahmoud Ahmadpour, Abdoul Ahad Cissé, and Mohammad Rokouei. "Applying bayesian population assessment models to artisanal, multispecies fisheries in the Northern Mokran Sea, Iran." Nature Conservation 28 (August 13, 2018): 61–89. https://doi.org/10.3897/natureconservation.28.25212.

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Small-scale fisheries substantially contribute to the reduction of poverty, local economies and food safety in many countries. However, limited and low-quality catches and effort data for small-scale fisheries complicate the stock assessment and management. Bayesian modelling has been advocated when assessing fisheries with limited data. Specifically, Bayesian models can incorporate information of the multiple sources, improve precision in the stock assessments and provide specific levels of uncertainty for estimating the relevant parameters. In this study, therefore, the state-space Bayesian

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Riedel, Michael, Stan E. Dosso, and Laurens Beran. "Uncertainty estimation for amplitude variation with offset (AVO) inversion." GEOPHYSICS 68, no. 5 (2003): 1485–96. http://dx.doi.org/10.1190/1.1620621.

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This paper uses a Bayesian approach for inverting seismic amplitude versus offset (AVO) data to provide estimates and uncertainties of the viscoelastic physical parameters at an interface. The inversion is based on Gibbs' sampling approach to determine properties of the posterior probability distribution (PPD), such as the posterior mean, maximum a posteriori (MAP) estimate, marginal probability distributions, and covariances. The Bayesian formulation represents a fully nonlinear inversion; the results are compared to those of standard linearized inversion. The nonlinear and linearized approac

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Smith, Jordan W., Lindsey S. Smart, Monica A. Dorning, Lauren Nicole Dupéy, Andréanne Méley, and Ross K. Meentemeyer. "Bayesian methods to estimate urban growth potential." Landscape and Urban Planning 163 (July 2017): 1–16. http://dx.doi.org/10.1016/j.landurbplan.2017.03.004.

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Sparacino, Giovanni, Stefano Milani, Edoardo Arslan, and Claudio Cobelli. "A Bayesian approach to estimate evoked potentials." Computer Methods and Programs in Biomedicine 68, no. 3 (2002): 233–48. http://dx.doi.org/10.1016/s0169-2607(01)00175-4.

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Han, Ming, and Yuanyao Ding. "Synthesized expected Bayesian method of parametric estimate." Journal of Systems Science and Systems Engineering 13, no. 1 (2004): 98–111. http://dx.doi.org/10.1007/s11518-006-0156-0.

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Kawahara, Daisuke, and Shigeyoshi Fujisawa. "Advantages of Persistent Cohomology in Estimating Animal Location From Grid Cell Population Activity." Neural Computation 36, no. 3 (2024): 385–411. http://dx.doi.org/10.1162/neco_a_01645.

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Abstract Many cognitive functions are represented as cell assemblies. In the case of spatial navigation, the population activity of place cells in the hippocampus and grid cells in the entorhinal cortex represents self-location in the environment. The brain cannot directly observe self-location information in the environment. Instead, it relies on sensory information and memory to estimate self-location. Therefore, estimating low-dimensional dynamics, such as the movement trajectory of an animal exploring its environment, from only the high-dimensional neural activity is important in decipheri

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de Lima, Max Sousa, and Gregorio Saravia Atuncar. "A Bayesian method to estimate the optimal bandwidth for multivariate kernel estimator." Journal of Nonparametric Statistics 23, no. 1 (2011): 137–48. http://dx.doi.org/10.1080/10485252.2010.485200.

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