Search results for "Bayesian Method"

GIGJ: a crustal gravity model of the Guangdong Province for predicting the geoneutrino signal at the JUNO experiment

2019

Gravimetric methods are expected to play a decisive role in geophysical modeling of the regional crustal structure applied to geoneutrino studies. GIGJ (GOCE Inversion for Geoneutrinos at JUNO) is a 3D numerical model constituted by ~46 x 10$^{3}$ voxels of 50 x 50 x 0.1 km, built by inverting gravimetric data over the 6{\deg} x 4{\deg} area centered at the Jiangmen Underground Neutrino Observatory (JUNO) experiment, currently under construction in the Guangdong Province (China). The a-priori modeling is based on the adoption of deep seismic sounding profiles, receiver functions, teleseismic P-wave velocity models and Moho depth maps, according to their own accuracy and spatial resolution. …

Clinical benefits of a Bayesian model for plasma-derived factor VIII/VWF after one year of pharmacokinetic-guided prophylaxis in severe/moderate hemo…

2021

Abstract Introduction Individual pharmacokinetic (PK) profiling in hemophilia A (HA) helps to individualize prophylaxis using population PK models (popPK). A specific popPK model for plasma-derived factor VIII containing von-Willebrand Factor (pdFVIII/VWF) was developed. Aim To compare standard versus PK-driven prophylaxis, using a generic or a specific popPK model for pdFVIII/VWF. Materials and methods A prospective study conducted in HA patients in prophylaxis with pdFVIII/VWF (Fanhdi®) comparing three one-year study periods: (1) standard prophylaxis, (2) PK-guided prophylaxis using a generic pdFVIII popPK model which described FVIII activity irrespective of FVIII concentrate, and (3) PK-…

The influence of the prior distribution on the uncertainty analysis assessment of an urban drainage stormwater quality model

2009

A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis.

2020

Many statistical models have been proposed to analyse small area disease data with the aim of describing spatial variation in disease risk. In this paper, we propose a Bayesian hierarchical model that simultaneously allows for risk estimation and cluster identification. Our model formulation assumes that there is an unknown number of risk classes and small areas are assigned to a risk class by means of independent allocation variables. Therefore, areas within each cluster are assumed to share a common risk but they may be geographically separated. The posterior distribution of the parameter representing the number of risk classes is estimated using a novel procedure that combines its prior …

Light-component spectrum of the primary cosmic rays in the multi-TeV region measured by the ARGO-YBJ experiment

2012

The ARGO-YBJ experiment detects extensive air showers in a wide energy range by means of a full-coverage detector which is in stable data taking in its full configuration since November 2007 at the YBJ International Cosmic Ray Observatory (4300 m a.s.l., Tibet, People's Republic of China). In this paper the measurement of the light-component spectrum of primary cosmic rays in the energy region $(5\textdiv{}200)\text{ }\text{ }\mathrm{TeV}$ is reported. The method exploited to analyze the experimental data is based on a Bayesian procedure. The measured intensities of the light component are consistent with the recent CREAM results and higher than that obtained adding the proton and helium sp…

Bayesian Checking of the Second Levels of Hierarchical Models

2007

Hierarchical models are increasingly used in many applications. Along with this increased use comes a desire to investigate whether the model is compatible with the observed data. Bayesian methods are well suited to eliminate the many (nuisance) parameters in these complicated models; in this paper we investigate Bayesian methods for model checking. Since we contemplate model checking as a preliminary, exploratory analysis, we concentrate on objective Bayesian methods in which careful specification of an informative prior distribution is avoided. Numerous examples are given and different proposals are investigated and critically compared.

Hierarchical Bayesian models for analysing fish biomass data. An application to Parapenaeus longirostris biomass data

2022

The Mediterranean International Trawl Survey (MEDITS) programme provides spatially referenced ecological data. We adopted a hierarchical Bayesian model to analyse Parapenaeus longirostris biomass data. The model comprises three parts, each of which identifies: the variability due to the explanatory variables, the variability due to the spatial domain (seen as a Gaussian Process) and the irregular component modelled as white noise. The estimated parameters show that some seabed characteristics affect biomass quantity and that the estimated behaviour of the Gaussian Process changes over different groups of years.

Physical and cognitive doping in university students using the unrelated question model (UQM): Assessing the influence of the probability of receivin…

2018

Study objectives: In order to increase the value of randomized response techniques (RRTs) as tools for studying sensitive issues, the present study investigated whether the prevalence estimate for a sensitive item π̂$_{s}$ assessed with the unrelated questionnaire method (UQM) is influenced by changing the probability of receiving the sensitive question p. Material and methods: A short paper-and-pencil questionnaire was distributed to 1.243 university students assessing the 12-month prevalence of physical and cognitive doping using two versions of the UQM with different probabilities for receiving the sensitive question (p ≈ 1/3 and p ≈ 2/3). Likelihood ratio tests were used to assess wheth…

Analysing the mediating role of a network: a Bayesian latent space approach

2020

The use of network analysis for the investigation of social structures has recently seen a rise, due both to the high availability of data and to the numerous insights it can provide into different fields. Most analyses focus on the topological characteristics of networks and the estimation of relationships between the nodes. We adopt a different point of view, by considering the whole network as a random variable conveying the effect of an exposure on a response. This point of view represents a classical mediation setting, where the interest lies in the estimation of the indirect effect, that is, the effect propagated through the mediating variable. We introduce a latent space model mappin…

A probabilistic compressive sensing framework with applications to ultrasound signal processing

2019

Abstract The field of Compressive Sensing (CS) has provided algorithms to reconstruct signals from a much lower number of measurements than specified by the Nyquist-Shannon theorem. There are two fundamental concepts underpinning the field of CS. The first is the use of random transformations to project high-dimensional measurements onto a much lower-dimensional domain. The second is the use of sparse regression to reconstruct the original signal. This assumes that a sparse representation exists for this signal in some known domain, manifested by a dictionary. The original formulation for CS specifies the use of an l 1 penalised regression method, the Lasso. Whilst this has worked well in l…