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Full-Text Articles in Physical Sciences and Mathematics
Objective Priors For Estimation Of Extended Exponential Geometric Distribution, Pedro L. Ramos, Fernando A. Moala, Jorge A. Achcar
Objective Priors For Estimation Of Extended Exponential Geometric Distribution, Pedro L. Ramos, Fernando A. Moala, Jorge A. Achcar
Journal of Modern Applied Statistical Methods
A Bayesian analysis was developed with different noninformative prior distributions such as Jeffreys, Maximal Data Information, and Reference. The aim was to investigate the effects of each prior distribution on the posterior estimates of the parameters of the extended exponential geometric distribution, based on simulated data and a real application.
Exonest: Bayesian Model Selection Applied To The Detection And Characterization Of Exoplanets Via Photometric Variations, Ben Placek, Kevin H. Knuth, Daniel Angerhausen
Exonest: Bayesian Model Selection Applied To The Detection And Characterization Of Exoplanets Via Photometric Variations, Ben Placek, Kevin H. Knuth, Daniel Angerhausen
Physics Faculty Scholarship
EXONEST is an algorithm dedicated to detecting and characterizing the photometric signatures of exoplanets, which include reflection and thermal emission, Doppler boosting, and ellipsoidal variations. Using Bayesian inference, we can test between competing models that describe the data as well as estimate model parameters. We demonstrate this approach by testing circular versus eccentric planetary orbital models, as well as testing for the presence or absence of four photometric effects. In addition to using Bayesian model selection, a unique aspect of EXONEST is the potential capability to distinguish between reflective and thermal contributions to the light curve. A case study is …
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Dynamic Bayesian Approaches To The Statistical Calibration Problem, Derick Lorenzo Rivers
Theses and Dissertations
The problem of statistical calibration of a measuring instrument can be framed both in a statistical context as well as in an engineering context. In the first, the problem is dealt with by distinguishing between the "classical" approach and the "inverse" regression approach. Both of these models are static models and are used to estimate "exact" measurements from measurements that are affected by error. In the engineering context, the variables of interest are considered to be taken at the time at which you observe the measurement. The Bayesian time series analysis method of Dynamic Linear Models (DLM) can be used …