Search results for "Bay"

Bayesian Essentials with R, 2nd edn. J.-M. Marin and C. P. Robert (2014). New York: Springer/Springer Texts in Statistics. 296 pages, ISBN: 978-1-461…

2015

Linear and ellipsoidal restrictions in linear regression

1991

The problem of combining linear and ellipsoidal restrictions in linear regression is investigated. Necessary and sufficient conditions for compactness of the restriction set are proved assuring the existence of a minimax estimator. When the restriction set is not compact a minimax estimator may still exist for special loss functions arid regression designs

Bayesian hierarchical models in manufacturing bulk service queues

2006

In this paper, Queueing Theory and Bayesian statistical tools are used to analyze the congestion of various manufacturing bulk service queues with the same characteristics that are working independently of one another and in equilibrium. Hierarchical models are discussed in order to develop the whole inferential process for the parameters governing the system. Markov Chain Monte Carlo methods and numerical inversion of transforms are addressed to compute the posterior predictive distributions of the usual measures of performance in practice.

Bayesian design in queues: An application to aeronautic maintenance

2007

We exploit Bayesian criteria for designing M/M/c//r queueing systems with spares. For illustration of our approach we use a real problem from aeronautic maintenance, where the numbers of repair crews and spare planes must be sufficiently large to meet the necessary operational capacity. Bayesian guarantees for this to happen can be given using predictive or posterior distributions.

Analysis of the renal transplant waiting list in the País Valencià (Spain).

2005

In this paper we analyse the renal transplant waiting list of the Pais Valencia in Spain, using Queueing theory. The customers of this queue are patients with end-stage renal failure waiting for a kidney transplant. We set up a simplified model to represent the flow of the customers through the system, and perform Bayesian inference to estimate parameters in the model. Finally, we consider several scenarios by tuning the estimations achieved and computationally simulate the behaviour of the queue under each one. The results indicate that the system could reach equilibrium at some point in the future and the model forecasts a slow decrease in the size of the waiting list in the short and mid…

Probabilistic small area risk assessment using GIS-based data: a case study on Finnish childhood diabetes

2000

A Bayesian hierarchical spatial model is constructed to describe the regional incidence of insulin dependent diabetes mellitus (IDDM) among the under 15-year-olds in Finland. The model exploits aggregated pixel-wise locations for both the cases and the population at risk. Typically such data arise from combining geographic information systems (GIS) with large databases. The dates of diagnosis and locations of the cases are observed from 1987 to 1996. The population at risk counts are available for every second year during the same period. A hierarchical model is suggested for the pixel wise case counts, including a population model to account for the uncertainty of the population at risk ov…

A Bayesian analysis of a queueing system with unlimited service

1997

Abstract A queueing system occurs when “customers” arrive at some facility requiring a certain type of “service” provided by the “servers”. Both the arrival pattern and the service requirements are usually taken to be random. If all the servers are busy when customers arrive, they usually wait in line to get served. Queues possess a number of mathematical challenges and have been mainly approached from a probability point of view, and statistical analysis are very scarce. In this paper we present a Bayesian analysis of a Markovian queue in which customers are immediately served upon arrival, and hence no waiting lines form. Emergency and self-service facilities provide many examples. Techni…

Fully Bayesian Approach to Image Restoration with an Application in Biogeography

1994

SUMMARY A common method of studying biogeographical ranges is an atlas survey, in which the research area is divided into a square grid and the data consist of the squares where observations occur. Often the observations form only an incomplete map of the true range, and a method is required to decide whether the blank squares indicate true absence or merely a lack of study there. This is essentially an image restoration problem, but it has properties that make the common empirical Bayesian procedures inadequate. Most notably, the observed image is heavily degraded, causing difficulties in the estimation of spatial interaction, and the assessment of reliability of the restoration is emphasi…

Distribucion final de referencia para el problema de Fieller-Creasy

1982

The problem of making inferences about the ratio of two normal populations is usually known as the Fieller-Creasy problem, and it gave rise to a controversy among fiducialists and confidence-intervalists. A Bayesian solution to such a problem when the two normal populations have the same unknown variance was presented by Bernardo (1977) using reference non-informative prior distributions. The solution to the case in which the variances are not assumed equal is obtained here. Some numerical results for artificial populations are given

Spatial moving average risk smoothing

2013

This paper introduces spatial moving average risk smoothing (SMARS) as a new way of carrying out disease mapping. This proposal applies the moving average ideas of time series theory to the spatial domain, making use of a spatial moving average process of unknown order to define dependence on the risk of a disease occurring. Correlation of the risks for different locations will be a function of m values (m being unknown), providing a rich class of correlation functions that may be reproduced by SMARS. Moreover, the distance (in terms of neighborhoods) that should be covered for two units to be found to make the correlation of their risks 0 is a quantity to be fitted by the model. This way, …