scholarly journals Bayesian Estimator for Partial Trajectory Alignment

2019 ◽  
Author(s):  
Przemyslaw A. Lasota ◽  
Julie A. Shah
Keyword(s):  
Bayesian Estimator

Stability of the Bayesian estimator of the Poisson mean under the inexactly specified gamma prior

1991 ◽  
Vol 12 (4) ◽  
pp. 329-333 ◽  
Author(s):  
Marek Męczarski ◽  
Ryszard Zieliński
Keyword(s):  
Bayesian Estimator

scholarly journals A Bayesian model for binary Markov chains

2004 ◽  
Vol 2004 (8) ◽  
pp. 421-429 ◽  
Author(s):  
Souad Assoudou ◽  
Belkheir Essebbar
Keyword(s):  
Monte Carlo ◽  
Markov Chain ◽  
Markov Chains ◽  
Bayesian Estimation ◽  
Bayesian Model ◽  
Transition Probabilities ◽  
Simulated Data ◽  
Bayesian Estimator ◽  
Jeffreys Prior ◽  
Data Set

This note is concerned with Bayesian estimation of the transition probabilities of a binary Markov chain observed from heterogeneous individuals. The model is founded on the Jeffreys' prior which allows for transition probabilities to be correlated. The Bayesian estimator is approximated by means of Monte Carlo Markov chain (MCMC) techniques. The performance of the Bayesian estimates is illustrated by analyzing a small simulated data set.

scholarly journals On Bayesian procedure for maximum earthquake magnitude estimation

2012 ◽  
Vol 2 (1) ◽  
pp. 7 ◽  
Author(s):  
Andrzej Kijko
Keyword(s):  
Maximum Likelihood ◽  
Posterior Distribution ◽  
Ad Hoc ◽  
Likelihood Function ◽  
Fundamental Problem ◽  
Likelihood Estimation ◽  
Earthquake Magnitude ◽  
Bayesian Estimator ◽  
Bayesian Procedure ◽  
Posterior Estimate

This work is focused on the Bayesian procedure for the estimation of the regional maximum possible earthquake magnitude <em>m</em><sub>max</sub>. The paper briefly discusses the currently used Bayesian procedure for m<sub>max</sub>, as developed by Cornell, and a statistically justifiable alternative approach is suggested. The fundamental problem in the application of the current Bayesian formalism for <em>m</em><sub>max</sub> estimation is that one of the components of the posterior distribution is the sample likelihood function, for which the range of observations (earthquake magnitudes) depends on the unknown parameter <em>m</em><sub>max</sub>. This dependence violates the property of regularity of the maximum likelihood function. The resulting likelihood function, therefore, reaches its maximum at the maximum observed earthquake magnitude <em>m</em><sup>obs</sup><sub>max</sub> and not at the required maximum <em>possible</em> magnitude <em>m</em><sub>max</sub>. Since the sample likelihood function is a key component of the posterior distribution, the posterior estimate of <em>m^</em><sub>max</sub> is biased. The degree of the bias and its sign depend on the applied Bayesian estimator, the quantity of information provided by the prior distribution, and the sample likelihood function. It has been shown that if the maximum posterior estimate is used, the bias is negative and the resulting underestimation of <em>m</em><sub>max</sub> can be as big as 0.5 units of magnitude. This study explores only the maximum posterior estimate of <em>m</em><sub>max</sub>, which is conceptionally close to the classic maximum likelihood estimation. However, conclusions regarding the shortfall of the current Bayesian procedure are applicable to all Bayesian estimators, <em>e.g.</em> posterior mean and posterior median. A simple, <em>ad hoc</em> solution of this non-regular maximum likelihood problem is also presented.

Noise-Enhanced Performance for an Optimal Bayesian Estimator

2004 ◽  
Vol 52 (5) ◽  
pp. 1327-1334 ◽  
Author(s):  
F. Chapeau-Blondeau ◽  
D. Rousseau
Keyword(s):  
Bayesian Estimator

Generalized pseudo-Bayesian estimator for networked control systems over UDP-like channels

2018 ◽  
Vol 355 (14) ◽  
pp. 6657-6674
Author(s):  
Shi Liang ◽  
Zhenyu Liu ◽  
Chan Qiu ◽  
Jianrong Tan
Keyword(s):  
Control Systems ◽  
Networked Control Systems ◽  
Networked Control ◽  
Bayesian Estimator

scholarly journals Forecasting Software Using Laplacian AR Model based on Bootstrap-Reversible Jump MCMC: Application on Stock Price Data

Webology ◽  
2021 ◽  
Vol 18 (Special Issue 04) ◽  
pp. 1045-1055
Author(s):  
Sup arman ◽  
Yahya Hairun ◽  
Idrus Alhaddad ◽  
Tedy Machmud ◽  
Hery Suharna ◽  
...  
Keyword(s):  
Stock Price ◽  
Complex Structure ◽  
Arma Model ◽  
Bayesian Estimator ◽  
Ar Model ◽  
Reversible Jump ◽  
Mcmc Algorithm ◽  
Reversible Jump Mcmc ◽  
Variable Dimension ◽  
Fixed Dimension

The application of the Bootstrap-Metropolis-Hastings algorithm is limited to fixed dimension models. In various fields, data often has a variable dimension model. The Laplacian autoregressive (AR) model includes a variable dimension model so that the Bootstrap-Metropolis-Hasting algorithm cannot be applied. This article aims to develop a Bootstrap reversible jump Markov Chain Monte Carlo (MCMC) algorithm to estimate the Laplacian AR model. The parameters of the Laplacian AR model were estimated using a Bayesian approach. The posterior distribution has a complex structure so that the Bayesian estimator cannot be calculated analytically. The Bootstrap-reversible jump MCMC algorithm was applied to calculate the Bayes estimator. This study provides a procedure for estimating the parameters of the Laplacian AR model. Algorithm performance was tested using simulation studies. Furthermore, the algorithm is applied to the finance sector to predict stock price on the stock market. In general, this study can be useful for decision makers in predicting future events. The novelty of this study is the triangulation between the bootstrap algorithm and the reversible jump MCMC algorithm. The Bootstrap-reversible jump MCMC algorithm is useful especially when the data is large and the data has a variable dimension model. The study can be extended to the Laplacian Autoregressive Moving Average (ARMA) model.

Bayesian Estimator for Angle Recovery: Event Classification and Reconstruction in Positron Emission Tomography

2007 ◽  
Author(s):  
Angela M K Foudray ◽  
Craig S Levin ◽  
Kevin H. Knuth ◽  
Ariel Caticha ◽  
Julian L. Center ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Emission Tomography ◽  
Bayesian Estimator ◽  
Event Classification ◽  
Positron Emission

scholarly journals Bayesian model and spatial analysis of oral and oropharynx cancer mortality in Minas Gerais, Brazil

2018 ◽  
Vol 23 (1) ◽  
pp. 153-160 ◽  
Author(s):  
Emílio Prado da Fonseca ◽  
Cláudia Di Lorenzo Oliveira ◽  
Francisco Chiaravalloti Neto ◽  
Antonio Carlos Pereira ◽  
Silvia Amélia Scudeler Vedovello ◽  
...  
Keyword(s):  
Spatial Analysis ◽  
Mortality Rate ◽  
Cancer Mortality ◽  
Bayesian Model ◽  
Spatial Epidemiology ◽  
Minas Gerais ◽  
Bayesian Estimator ◽  
Empirical Bayesian ◽  
Oropharynx Cancer ◽  
South Central

Abstract The objective of this study was to determine of oral and oropharynx cancer mortality rate and the results were analyzed by applying the Spatial Analysis of Empirical Bayesian Model. To this end, we used the information contained in the International Classification of Diseases (ICD-10), Chapter II, Category C00 to C14 and Brazilian Mortality Information System (SIM) of Minas Gerais State. Descriptive statistics were observed and the gross rate of mortality was calculated for each municipality. Then Empirical Bayesian estimators were applied. The results showed that, in 2012, in the state of Minas Gerais, were registered 769 deaths of patients with cancer of oral and oropharynx, with 607 (78.96%) men and 162 (21.04%) women. There was a wide variation in spatial distribution of crude mortality rate and were identified agglomeration in the South, Central and North more accurately by Bayesian Estimator Global and Local Model. Through Bayesian models was possible to map the spatial clustering of deaths from oral cancer more accurately, and with the application of the method of spatial epidemiology, it was possible to obtain more accurate results and provide subsidies to reduce the number of deaths from this type of cancer.

Sign in / Sign up

Export Citation Format

Share Document