Search results for "Bayesian [statistical analysis]"
showing 10 items of 299 documents
Finding Prediction Limits for a Future Number of Failures in the Prescribed Time Interval under Parametric Uncertainty
2012
Computing prediction intervals is an important part of the forecasting process intended to indicate the likely uncertainty in point forecasts. Prediction intervals for future order statistics are widely used for reliability problems and other related problems. In this paper, we present an accurate procedure, called ‘within-sample prediction of order statistics', to obtain prediction limits for the number of failures that will be observed in a future inspection of a sample of units, based only on the results of the first in-service inspection of the same sample. The failure-time of such units is modeled with a two-parameter Weibull distribution indexed by scale and shape parameters β and δ, …
Solving two‐armed Bernoulli bandit problems using a Bayesian learning automaton
2010
PurposeThe two‐armed Bernoulli bandit (TABB) problem is a classical optimization problem where an agent sequentially pulls one of two arms attached to a gambling machine, with each pull resulting either in a reward or a penalty. The reward probabilities of each arm are unknown, and thus one must balance between exploiting existing knowledge about the arms, and obtaining new information. The purpose of this paper is to report research into a completely new family of solution schemes for the TABB problem: the Bayesian learning automaton (BLA) family.Design/methodology/approachAlthough computationally intractable in many cases, Bayesian methods provide a standard for optimal decision making. B…
Reproducing kernel hilbert spaces regression methods for genomic assisted prediction of quantitative traits.
2008
Abstract Reproducing kernel Hilbert spaces regression procedures for prediction of total genetic value for quantitative traits, which make use of phenotypic and genomic data simultaneously, are discussed from a theoretical perspective. It is argued that a nonparametric treatment may be needed for capturing the multiple and complex interactions potentially arising in whole-genome models, i.e., those based on thousands of single-nucleotide polymorphism (SNP) markers. After a review of reproducing kernel Hilbert spaces regression, it is shown that the statistical specification admits a standard mixed-effects linear model representation, with smoothing parameters treated as variance components.…
Radiocarbono y estadística Bayesiana: aportaciones a la cronología de la Edad del Bronce en el extremo oriental del sudeste de la península Ibérica
2014
La investigación arqueológica desarrollada en las últimas décadas ha permitido evaluar que en los valles de los ríos Segura y Vinalopó se dirimió el contacto entre dos sociedades de la Edad del Bronce de la península Ibérica: el grupo Argárico y el grupo del Prebético Meridional Valenciano. Las excavaciones realizadas en tres yacimientos de este ámbito - Terlinques, Cabezo Pardo y Cabezo Redondo- y las dotaciones de radiocarbono obtenidas permiten por primera vez evaluar la diacronía del proceso histórico que envolvió el desarrollo de ambos grupos arqueológicos a lo largo del II milenio cal BC, así como determinar diversos momentos socialmente significativos en su devenir histórico. Para el…
Socio-economic deprivation and COVID-19 infection: a Bayesian spatial modelling approach
2022
Il presente articolo ha l’obiettivo di analizzare l’effetto della deprivazione socio-economica sull’incidenza da COVID-19 a livello sub-comunale. Grazie alla disponibilit`a di informazioni sui tassi di incidenza mensili da COVID-19 a livello di sezione di censimento per i due comuni di Palermo e Catania (Italia), viene pro- posto l’utilizzo di un modello spaziale Bayesiano con distribuzione binomiale zero- inflated. I risultati mostrano un’associazione tra livelli di deprivazione e incidenza da COVID-19 nei due comuni, controllando per la struttura spaziale delle unit`a areali considerate. Alla luce dei risultati, si rendono necessarie azioni di politica sanitaria focalizzando gli intervent…
Whole-Genome Re-Sequencing Data to Infer Historical Demography and Speciation Processes in Land Snails: the Study of Two Candidula Sister Species
2021
Despite the global biodiversity of terrestrial gastropods and their ecological and economic importance, the genomic basis of ecological adaptation and speciation in land snail taxa is still largely unknown. Here, we combined whole-genome re-sequencing with population genomics to evaluate the historical demography and the speciation process of two closely related species of land snails from western Europe, Candidula unifasciata and C. rugosiuscula. Historical demographic analysis indicated fluctuations in the size of ancestral populations, probably driven by Pleistocene climatic fluctuations. Although the current population distributions of both species do not overlap, our approximate Bayesi…
Robustified smoothing for enhancement of thermal image sequences affected by clouds
2015
Obtaining radiometric surface temperature information with both high acquisition rate and high spatial resolution is still not possible through a single sensor. However, in several earth observation applications, the fusion of data acquired by different sensors is a viable solution for so called image sharpening. A related issue is the presence of clouds, which may impair the performance of the data fusion algorithms. In this paper we propose a robustified setup for the sharpening of thermal images in a non real-time scenario, capable to deal with missing thermal data due to cloudy pixels, and robust with respect to cloud mask misclassifications. The effectiveness of the presented technique…
Bayesian versus data driven model selection for microarray data
2014
Clustering is one of the most well known activities in scientific investigation and the object of research in many disciplines, ranging from Statistics to Computer Science. In this beautiful area, one of the most difficult challenges is a particular instance of the model selection problem, i.e., the identification of the correct number of clusters in a dataset. In what follows, for ease of reference, we refer to that instance still as model selection. It is an important part of any statistical analysis. The techniques used for solving it are mainly either Bayesian or data-driven, and are both based on internal knowledge. That is, they use information obtained by processing the input data. A…
Channel selection in Cognitive Radio Networks: A Switchable Bayesian Learning Automata approach
2013
We consider the problem of a user operating within a Cognitive Radio Network (CRN) which involves N channels each associated with a Primary User (PU). The problem consists of allocating a channel which, at any given time instant is not being used by a PU, to a Secondary User (SU). Within our study, we assume that a SU is allowed to perform “channel switching”, i.e., to choose an alternate channel S times (where S +1 ≤ N) if the previous choice does not lead to a channel which is vacant. The paper first presents a formal probabilistic model for the problem itself, referred to as the Formal Secondary Channel Selection (FSCS) problem, and the characteristics of the FSCS are then analyzed. Ther…
Bayesian inference in Markovian queues
1994
This paper is concerned with the Bayesian analysis of general queues with Poisson input and exponential service times. Joint posterior distribution of the arrival rate and the individual service rate is obtained from a sample consisting inn observations of the interarrival process andm complete service times. Posterior distribution of traffic intensity inM/M/c is also obtained and the statistical analysis of the ergodic condition from a decision point of view is discussed.