Search results for "BAYESIAN"
showing 10 items of 604 documents
Bayesian Analysis of Population Health Data
2021
The analysis of population-wide datasets can provide insight on the health status of large populations so that public health officials can make data-driven decisions. The analysis of such datasets often requires highly parameterized models with different types of fixed and random effects to account for risk factors, spatial and temporal variations, multilevel effects and other sources on uncertainty. To illustrate the potential of Bayesian hierarchical models, a dataset of about 500,000 inhabitants released by the Polish National Health Fund containing information about ischemic stroke incidence for a 2-year period is analyzed using different types of models. Spatial logistic regression and…
Can visualization alleviate dichotomous thinking? Effects of visual representations on the cliff effect
2021
Common reporting styles for statistical results in scientific articles, such as $p$ p -values and confidence intervals (CI), have been reported to be prone to dichotomous interpretations, especially with respect to the null hypothesis significance testing framework. For example when the $p$ p -value is small enough or the CIs of the mean effects of a studied drug and a placebo are not overlapping, scientists tend to claim significant differences while often disregarding the magnitudes and absolute differences in the effect sizes. This type of reasoning has been shown to be potentially harmful to science. Techniques relying on the visual estimation of the strength of evidence have been recom…
Robust Conditional Independence maps of single-voxel Magnetic Resonance Spectra to elucidate associations between brain tumours and metabolites.
2020
The aim of the paper is two-fold. First, we show that structure finding with the PC algorithm can be inherently unstable and requires further operational constraints in order to consistently obtain models that are faithful to the data. We propose a methodology to stabilise the structure finding process, minimising both false positive and false negative error rates. This is demonstrated with synthetic data. Second, to apply the proposed structure finding methodology to a data set comprising single-voxel Magnetic Resonance Spectra of normal brain and three classes of brain tumours, to elucidate the associations between brain tumour types and a range of observed metabolites that are known to b…
A study on the degree of relationship between two individuals.
2000
The paper studies the likely degree of relationship between two individuals who could possibly be half sibs. The possible common ancestor was dead, which further complicated the problem. The model used was devised by Thompson [in Rao and Chakraborty (eds): Handbook of Statistics, North-Holland, Amsterdam, 1991] and establishes a correspondence between the possible degree of relationship and certain feasible probability distributions on the number of identical by descent genes. Two statistical approaches are considered: the classical one, in which the maximum likelihood estimation for the parameters of Thompson’s model are obtained, and the Bayesian one, in which the test of the hypothesis o…
Robustness of the risk–return relationship in the U.S. stock market
2008
Abstract Using GARCH-in-Mean models, we study the robustness of the risk–return relationship in monthly U.S. stock market returns (1928:1–2004:12) with respect to the specification of the conditional mean equation. The issue is important because in this commonly used framework, unnecessarily including an intercept is known to distort conclusions. The existence of the relationship is relatively robust, but its strength depends on the prior belief concerning the intercept. The latter applies in particular to the first half of the sample, where also the coefficient of the relative risk aversion is smaller and the equity premium greater than in the latter half.
Prediction and Surveillance Sampling Assessment in Plant Nurseries and Fields
2022
In this paper, we propose a structured additive regression (STAR) model for modeling the occurrence of a disease in fields or nurseries. The methodological approach involves a Gaussian field (GF) affected by a spatial process represented by an approximation to a Gaussian Markov random field (GMRF). This modeling allows the building of maps with prediction probabilities regarding the presence of a disease in plants using Bayesian kriging. The advantage of this modeling is its computational benefit when compared with known spatial hierarchical models and with the Bayesian inference based on Markov chain Monte Carlo (MCMC) methods. Inference through the use of the integrated nested Laplace app…
Towards an Assembly Support System with Dynamic Bayesian Network
2022
Due to the new technological advancements and the adoption of Industry 4.0 concepts, the manufacturing industry is now, more than ever, in a continuous transformation. This work analyzes the possibility of using dynamic Bayesian networks to predict the next assembly steps within an assembly assistance training system. The goal is to develop a support system to assist the human workers in their manufacturing activities. The evaluations were performed on a dataset collected from an experiment involving students. The experimental results show that dynamic Bayesian networks are appropriate for such a purpose, since their prediction accuracy was among the highest on new patterns. Our dynamic Bay…
Non-communicable diseases, socio-economic status, lifestyle and well-being in Italy: An additive Bayesian network model
2018
The aim of the paper is to investigate the statistical association, on a sample of Italian subjects, extracted by Survey of Health, Ageing and Retirement in Europe (SHARE) dataset, between chronic diseases (occurrence or number of chronic diseases) and socio-economic and behavioural determinants (lifestyle indicators, QoL indicators, cognitive functioning variables). To this aim, additive Bayesian network (ABN) analysis was used. The resulting ABN model shows that better educated individuals have better health outcomes, age is direct and gender is an indirect determinant of the number of chronic diseases. Furthermore, self-perceived health is associated with lower number of chronic diseases…
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.
Relative risk estimation of dengue disease at small spatial scale
2017
Abstract Background Dengue is a high incidence arboviral disease in tropical countries around the world. Colombia is an endemic country due to the favourable environmental conditions for vector survival and spread. Dengue surveillance in Colombia is based in passive notification of cases, supporting monitoring, prediction, risk factor identification and intervention measures. Even though the surveillance network works adequately, disease mapping techniques currently developed and employed for many health problems are not widely applied. We select the Colombian city of Bucaramanga to apply Bayesian areal disease mapping models, testing the challenges and difficulties of the approach. Methods…