Results 1 to 10 of about 1,665,365 (169)
Objective Bayesian Estimation for Tweedie Exponential Dispersion Process
Mathematics, 2021 An objective Bayesian method for the Tweedie Exponential Dispersion (TED) process model is proposed in this paper. The TED process is a generalized stochastic process, including some famous stochastic processes (e.g., Wiener, Gamma, and Inverse Gaussian ...
Weian Yan +3 more
doaj +1 more source
Intrinsic Bayesian estimation of linear time series models
Statistical Theory and Related Fields, 2021 Intrinsic loss functions (such as the Kullback–Leibler divergence, i.e. the entropy loss) have been used extensively in place of conventional loss functions for independent samples. But applications in serially correlated samples are scant.
Shawn Ni, Dongchu Sun
doaj +1 more source
Geometric properties of noninformative priors based on the chi-square divergence
Frontiers in Applied Mathematics and Statistics, 2023 Recently, a noninformative prior distribution that is different from the Jeffreys prior was derived as an extension of Bernardo's reference prior based on the chi-square divergence.
Fuyuhiko Tanaka, Fuyuhiko Tanaka
doaj +1 more source
Reference priors with partial information [PDF]
Biometrika, 1998 zbMATH Open Web Interface contents unavailable due to conflicting licenses.
Sun, D., Berger, J. O.
openaire +2 more sources
Current Frame Priors Assisted Neural Network for Intra Prediction
IEEE Access, 2021 Intra prediction is the key technology to reduce spatial redundancy in the modern video coding standard. Recently, deep learning based methods that directly generate the intra prediction by neural network achieve superior performance than traditional ...
Han Zhang, Li Song, Yan Huang, Rong Xie
doaj +1 more source
Approximate reference priors for Gaussian random fields [PDF]
Scandinavian Journal of Statistics, 2022 AbstractReference priors are theoretically attractive for the analysis of geostatistical data since they enable automatic Bayesian analysis and have desirable Bayesian and frequentist properties. But their use is hindered by computational hurdles that make their application in practice challenging.
Victor De Oliveira, Zifei Han
openaire +2 more sources
Bayesian analysis for the Lomax model using noninformative priors
Statistical Theory and Related Fields, 2023 The Lomax distribution is an important member in the distribution family. In this paper, we systematically develop an objective Bayesian analysis of data from a Lomax distribution. Noninformative priors, including probability matching priors, the maximal
Daojiang He, Dongchu Sun, Qing Zhu
doaj +1 more source
Viruses, 2022 Prior vaccination can alternately enhance or attenuate influenza vaccine immunogenicity and effectiveness. Analogously, we found that vaccine immunogenicity was enhanced by prior A(H3N2) virus infection among participants of the Ha Nam Cohort, Viet Nam ...
Annette Fox +12 more
doaj +1 more source
On Default Priors for Robust Bayesian Estimation with Divergences
Entropy, 2020 This paper presents objective priors for robust Bayesian estimation against outliers based on divergences. The minimum γ-divergence estimator is well-known to work well in estimation against heavy contamination. The robust Bayesian methods by using quasi-
Tomoyuki Nakagawa, Shintaro Hashimoto
doaj +1 more source
IEEE Access, 2023 A robot localization problem demands a fair comparison of the positioning algorithms. A reference trajectory of the robot’s movement is needed to estimate errors and evaluate a quality of the localization.
Mark Griguletskii +5 more
doaj +1 more source