5 resultados para Non informative priors


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Radiocarbon dating is routinely used in paleoecology to build chronolo- gies of lake and peat sediments, aiming at inferring a model that would relate the sediment depth with its age. We present a new approach for chronology building (called “Bacon”) that has received enthusiastic attention by paleoecologists. Our methodology is based on controlling core accumulation rates using a gamma autoregressive semiparametric model with an arbitrary number of subdivisions along the sediment. Using prior knowledge about accumulation rates is crucial and informative priors are routinely used. Since many sediment cores are currently analyzed, using different data sets and prior distributions, a robust (adaptive) MCMC is very useful. We use the t-walk (Christen and Fox, 2010), a self adjusting, robust MCMC sampling algorithm, that works acceptably well in many situations. Outliers are also addressed using a recent approach that considers a Student-t model for radiocarbon data. Two examples are presented here, that of a peat core and a core from a lake, and our results are compared with other approaches.

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This paper considers inference from multinomial data and addresses the problem of choosing the strength of the Dirichlet prior under a mean-squared error criterion. We compare the Maxi-mum Likelihood Estimator (MLE) and the most commonly used Bayesian estimators obtained by assuming a prior Dirichlet distribution with non-informative prior parameters, that is, the parameters of the Dirichlet are equal and altogether sum up to the so called strength of the prior. Under this criterion, MLE becomes more preferable than the Bayesian estimators at the increase of the number of categories k of the multinomial, because non-informative Bayesian estimators induce a region where they are dominant that quickly shrinks with the increase of k. This can be avoided if the strength of the prior is not kept constant but decreased with the number of categories. We argue that the strength should decrease at least k times faster than usual estimators do.

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Cascade control is one of the routinely used control strategies in industrial processes because it can dramatically improve the performance of single-loop control, reducing both the maximum deviation and the integral error of the disturbance response. Currently, many control performance assessment methods of cascade control loops are developed based on the assumption that all the disturbances are subject to Gaussian distribution. However, in the practical condition, several disturbance sources occur in the manipulated variable or the upstream exhibits nonlinear behaviors. In this paper, a general and effective index of the performance assessment of the cascade control system subjected to the unknown disturbance distribution is proposed. Like the minimum variance control (MVC) design, the output variances of the primary and the secondary loops are decomposed into a cascade-invariant and a cascade-dependent term, but the estimated ARMA model for the cascade control loop based on the minimum entropy, instead of the minimum mean squares error, is developed for non-Gaussian disturbances. Unlike the MVC index, an innovative control performance index is given based on the information theory and the minimum entropy criterion. The index is informative and in agreement with the expected control knowledge. To elucidate wide applicability and effectiveness of the minimum entropy cascade control index, a simulation problem and a cascade control case of an oil refinery are applied. The comparison with MVC based cascade control is also included.

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We apply the framework of non-equilibrium quantum thermodynamics to the physics of quenched small-size bosonic quantum gases in a harmonic trap. By studying the temporal behaviour of the Loschmidt echo and of the atomic density profile within the trap, which are informative of the non-equilibrium physics and the correlations among the particles, we establish a link with the statistics of (irreversible) work done on the system. This highlights interesting connections between the degree of inter-particle entanglement and the non-equilibrium thermodynamics of the system.

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INTRODUCTION: EGFR screening requires good quality tissue, sensitivity and turn-around time (TAT). We report our experience of routine screening, describing sample type, TAT, specimen quality (cellularity and DNA yield), histopathological description, mutation result and clinical outcome. METHODS: Non-small cell lung cancer (NSCLC) sections were screened for EGFR mutations (M+) in exons 18-21. Clinical, pathological and screening outcome data were collected for year 1 of testing. Screening outcome alone was collected for year 2. RESULTS: In year 1, 152 samples were tested, most (72%) were diagnostic. TAT was 4.9 days (95%confidence interval (CI)=4.5-5.5). EGFR-M+ prevalence was 11% and higher (20%) among never-smoking women with adenocarcinomas (ADCs), but 30% of mutations occurred in current/ex-smoking men. EGFR-M+ tumours were non-mucinous ADCs and 100% thyroid transcription factor (TTF1+). No mutations were detected in poorly differentiated NSCLC-not otherwise specified (NOS). There was a trend for improved overall survival (OS) among EGFR-M+ versus EGFR-M- patients (median OS=78 versus 17 months). In year 1, test failure rate was 19%, and associated with scant cellularity and low DNA concentrations. However 75% of samples with poor cellularity but representative of tumour were informative and mutation prevalence was 9%. In year 2, 755 samples were tested; mutation prevalence was 13% and test failure only 5.4%. Although samples with low DNA concentration (2.2 ng/μL), the mutation rate was 9.2%. CONCLUSION: Routine epidermal growth factor receptor (EGFR) screening using diagnostic samples is fast and feasible even on samples with poor cellularity and DNA content. Mutations tend to occur in better-differentiated non-mucinous TTF1+ ADCs. Whether these histological criteria may be useful to select patients for EGFR testing merits further investigation.