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Least-Squares Approximation in Bayesian Analysis
Scientific reasoning and probability: a comparison between bayesianism and error statistics
O artigo apresenta os principais elementos de dois enfoques alternativos para o uso do cálculo de probabilidades na análise do raciocínio científico: o bayesianismo e a estatística do erro. O debate entre essas correntes é um dos mais relevantes da filosofia da ciência contemporânea e constitui uma...
Model uncertainty and variable selection: an application to the modelization of FDI determinants in Europe
Las últimas décadas han visto un interés cada vez mayor en la IED, y un debate creciente sobre su modelización en términos de las variables consideradas como sus determinantes, la especificación del modelo y los métodos de estimación del modelo de gravedad de la IED. Esto se debe a la incertidumbre...
Bayesian Hierarchical Approach To Estimate the Risk of Radiation-Induced Cancers in the Situation of Multiple and Uncertain Occupational Exposures. Application to Workers in the Nuclear Fuel Cycle
Nuclear fuel cycle workers are chronically exposed to multiple radiological sources. To date, the cancer risks associated with simultaneous and often correlated exposures have been rarely estimated and the measurement uncertainties on these exposures rarely considered. The aim of this work is to pro...
Modelling and forecasting monthly Brent crude oil prices: a long memory and volatility approach
The Standard Generalised Autoregressive Conditionally Heteroskedastic (sGARCH) model and the Functional Generalised Autoregressive Conditionally Heteroskedastic (fGARCH) model were applied to study the volatility of the Autoregressive Fractionally Integrated Moving Average (ARFIMA) model, which is t...
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MultiBaC: A strategy to remove batch effects between different omic data types
Remarks on Bayesian Networks and Their Applications
Bayesian networks are directed acyclic graphs that represent dependencies between variables in a probabilistic model. They are becoming an increasingly important area fог research and applications in the entire field of Artificial Intelligence. This paper explores the nature of implications for Baye...
Personalized prediction and machine learning methods in tools for web computation
One of the main characteristics of the modes of computation known as big data concern the generalization of machnie learning methods. They offer to calculate the society in a way that do not match the requirements of centrality, univocity and generality of statistical methods, plotting individuals a...
A Two-Component Normal Mixture Alternative to the Fay-Herriot Model
This article considers a robust hierarchical Bayesian approach to deal with random effects of small area means when some of these effects assume extreme values, resulting in outliers. In the presence of outliers, the standard Fay-Herriot model, used for modeling area-level data, under normality assu...
Markovian modeling for dependent interrecurrence times in bladder cancer
A Simple Model of Monetary Policy under Phillips-Curve Causal Disagreements
I study a static textbook model of monetary policy and relax the conventional assumption that the private sector has rational expectations. Instead, the private sector forms inflation forecasts according to a misspecified subjective model that disagrees with the central bank's (true) model over the...
Bayesian Hierarchical Approach to Deal with Protracted Low-Dose Exposure Measurement Errors to Ionizing Radiations in Estimating the Risk of Radiation-Induced Cancers : Application to a Uranium Miners Cohort
In radiation epidemiology, exposure measurement error and uncertain input parameters in the calculation of absorbed organ doses are among the most important sources of uncertainty in the modelling of the health effects of ionising radiation. As the structures of exposure and dose uncertainty arising...
New statistical framework for fine-mapping
Des études d’association à l’échelle du génome (GWAS) ont permis l’identification de milliers de régions du génome comportant des variants génétiques associés à des traits et qui peuvent être à l’origine de certaines maladies complexes. Cependant faire des tests biologiques pour tous les variants gé...
An Integrated Approach to the Realization of Saudi Arabia’s Energy Sustainability
As system thinking is a recognized approach to the comprehension and realization of energy sustainability, this paper applies a holistic representation to the World Energy Trilemma Index (WETI) key indicators using Bayesian Belief Networks (BBN) to illuminate the probabilistic information of their i...
Essays on econometric modelling of temporal networks
Graph theory has long been studied in mathematics and probability as a tool for describing dependence between nodes. However, only recently it has been implemented on data, giving birth to the statistical analysis of real networks.The topology of economic and financial networks is remarkably complex...
Renerend Bayes, Bioennology and babies
This article will attempt to show that it is not possible to question the infant’s experience during the first weeks of life by observing his or her behavior only. Data from Bayesian cognitive science and phenomenology offer us a much more relevant perspective on the infant’s experience during this...
Multi-item surveys are frequently used to study scores on latent factors, like human values, attitudes and behavior. Such studies often include a comparison, between specific groups of individuals, either at one or multiple points in time. If such latent factor means are to be meaningfully compared,...
Bayesian Inference for State Space Model with Panel Data
The present work explores panel data set-up in a Bayesian state space model. The conditional posterior densities of parameters are utilized to determine the marginal posterior densities using the Gibbs sampler. An efficient one step ahead predictive density mechanism is developed to further the stat...
Evidence of hypotheses by bayes factor: examples of use in empirical studies
Statistical tests are used in science in order to support research hypotheses (theory, model). The Bayes Factor (BF) is a method that weighs evidence and shows which out of two hypotheses is better supported. Adopting the BF in statistical inference, we can show whether data provided stronger suppor...
Bayesian spatial modelling for high dimensional seismic inverse problems
Study of Bayesian optimisation techniques applied to painting operations with robotic manipulators
[EN] This Master Thesis will explore the computation of optimal parameters for simulated robot maneuvers in CoppeliaSim using the Bayesian Optimization of Matlab's Statistics and Machine Learning Toolbox. The simulation environment, communication with Matlab and the statistical analysis of results w...
Recenzja: John C. Kruschke, Doing Bayesian Data Analysis: A Tutorial with R, Jags and Stan, Academic Press, San Diego CA 2014, Kindle edition, ss. 776.
Od pewnego czasu w naukach społęcznych rośnie zainteresowanie statystyką bayesowską i metodami symulacji Monte Carlo. W konsekwencji powstaje coraz silniejsze zapotrzebowanie na podręczniki, które przyblizyłyby te zagadnienia. Jedną z takich propozycji jest książka Johna K. Kruschke pt. Doing Bayesi...
Variational Approximations for Selecting Hierarchical Models of Circular Data in a Small Area Estimation Application
We consider hierarchical regression models for circular data using the projected normal distribution, applied in the development of weights for the Access Point Angler Intercept Survey, a recreational angling survey conducted by the US National Marine Fisheries Service. Weighted estimates of recreat...
A statistical framework for the validation of a population exposure model based on personal exposure data
International audience Currently, ambient pollutant concentrations at monitoring sites are routinely measured by local networks, such as AIRPARIF in Paris, France. Pollutant concentration fields are also simulated with regional-scale chemistry transport models such as CHIMERE (http://www.lmd.polytec...
A Bayesian estimation of the Gini index and the Bonferroni index for the Dagum distribution with the application of different priors
Bayesian estimators and highest posterior density credible intervals are obtained for two popular inequality measures, viz. the Gini index and the Bonferroni index in the case of the Dagum distribution. The study considers informative and non-informative priors, i.e. the Mukherjee-Islam prior and th...