Search results for " Sam"
showing 10 items of 1096 documents
Group Metropolis Sampling
2017
Monte Carlo (MC) methods are widely used for Bayesian inference and optimization in statistics, signal processing and machine learning. Two well-known class of MC methods are the Importance Sampling (IS) techniques and the Markov Chain Monte Carlo (MCMC) algorithms. In this work, we introduce the Group Importance Sampling (GIS) framework where different sets of weighted samples are properly summarized with one summary particle and one summary weight. GIS facilitates the design of novel efficient MC techniques. For instance, we present the Group Metropolis Sampling (GMS) algorithm which produces a Markov chain of sets of weighted samples. GMS in general outperforms other multiple try schemes…
Recycling Gibbs sampling
2017
Gibbs sampling is a well-known Markov chain Monte Carlo (MCMC) algorithm, extensively used in signal processing, machine learning and statistics. The key point for the successful application of the Gibbs sampler is the ability to draw samples from the full-conditional probability density functions efficiently. In the general case this is not possible, so in order to speed up the convergence of the chain, it is required to generate auxiliary samples. However, such intermediate information is finally disregarded. In this work, we show that these auxiliary samples can be recycled within the Gibbs estimators, improving their efficiency with no extra cost. Theoretical and exhaustive numerical co…
Theoretical Foundations of the Monte Carlo Method and Its Applications in Statistical Physics
2002
In this chapter we first introduce the basic concepts of Monte Carlo sampling, give some details on how Monte Carlo programs need to be organized, and then proceed to the interpretation and analysis of Monte Carlo results.
Clinically-Driven Virtual Patient Cohorts Generation: An Application to Aorta
2021
The combination of machine learning methods together with computational modeling and simulation of the cardiovascular system brings the possibility of obtaining very valuable information about new therapies or clinical devices through in-silico experiments. However, the application of machine learning methods demands access to large cohorts of patients. As an alternative to medical data acquisition and processing, which often requires some degree of manual intervention, the generation of virtual cohorts made of synthetic patients can be automated. However, the generation of a synthetic sample can still be computationally demanding to guarantee that it is clinically meaningful and that it re…
A Software Package for a Serum Bank Management
1979
A serum-bank is a collection of human serum samples coming from different locations (in our case Children Hospital, schools, factories, town departemens), allocated in some archives. Principal users of a such data-bank are, of course, physicians and biologists that are mainly interested in statistical analysis (computation of averages, variances factor analysis, etc.) of immunological and epidemiological relevance, in order to investigate about some haematochemical parameters common to some selected subset of the archives [1], [2].
Efficient anomaly detection on sampled data streams with contaminated phase I data
2020
International audience; Control chart algorithms aim to monitor a process over time. This process consists of two phases. Phase I, also called the learning phase, estimates the normal process parameters, then in Phase II, anomalies are detected. However, the learning phase itself can contain contaminated data such as outliers. If left undetected, they can jeopardize the accuracy of the whole chart by affecting the computed parameters, which leads to faulty classifications and defective data analysis results. This problem becomes more severe when the analysis is done on a sample of the data rather than the whole data. To avoid such a situation, Phase I quality must be guaranteed. The purpose…
Dog-bite-related attacks: A new forensic approach
2020
Dog attacks today represent a health hazard considering that prevention strategies have not always been successful. The identification of the dog that attacked the victim is necessary, considering the civil or criminal consequences for the animal's owner. An accurate scene analysis must be performed collecting a series of important information.Forensic investigations in dog attacks involve different methods, such as the evaluating of the canine Short Tandem Repeat (STR) typing in saliva traces on wounds or bite mark analysis, however, these techniques cannot always be applied. The effort to find new methods to identify the dog that attacked the victim represents a very interesting field for…
Portability in analytical chemistry: a green and democratic way for sustainability
2019
International audience; Recent advances in portability of analytical equipment have been considered to enlighten the advantages offered by portable instrumentation on greening the analytical methods. Their use drastically reduces sampling, sample stockage, and transport, thus avoiding environmental side effects and risks, also improving decision-making. The fact that portable instrumentation is, in general, less expensive than bench instruments and apparatuses makes also available the analytical tools for extended sectors of the population, thus making accessible the advantages derived from analytical methods. The role of sensor technology and portable miniaturized systems has been consider…
Cooperative compressive power spectrum estimation in wireless fading channels
2017
This paper considers multiple wireless sensors that cooperatively estimate the power spectrum of the signals received from several sources. We extend our previous work on cooperative compressive power spectrum estimation to accommodate the scenario where the statistics of the fading channels experienced by different sensors are different. The signals received from the sources are assumed to be time-domain wide-sense stationary processes. Multiple sensors are organized into several groups, where each group estimates a different subset of lags of the temporal correlation. A fusion centre (FC) combines these estimates to obtain the power spectrum. As each sensor group computes correlation esti…
Revenue models, in-app purchase, and the app performance: Evidence from Apple's App Store and Google Play
2016
The effect of revenue models on app performance depends on the app store.The effect of in-app purchase on app performance depends on the app store.Paid, freemium and in-purchase models are shown to be effective in app store.Freemium and in-app purchase models are shown to be less effective in Google Play.App category influences the effects of both revenue models and in-app purchase. In this paper, we empirically examine how the revenue model (paid, free, or freemium) adopted for a given app affects the app revenue performance as measured by the app daily revenue rank. We also study the impact of in-app purchase on this measure of performance. Moreover, we study how such relationships are co…