Search results for " 62J05"

showing 3 items of 3 documents

A Bayesian Multilevel Random-Effects Model for Estimating Noise in Image Sensors

2020

Sensor noise sources cause differences in the signal recorded across pixels in a single image and across multiple images. This paper presents a Bayesian approach to decomposing and characterizing the sensor noise sources involved in imaging with digital cameras. A Bayesian probabilistic model based on the (theoretical) model for noise sources in image sensing is fitted to a set of a time-series of images with different reflectance and wavelengths under controlled lighting conditions. The image sensing model is a complex model, with several interacting components dependent on reflectance and wavelength. The properties of the Bayesian approach of defining conditional dependencies among parame…

FOS: Computer and information sciencesMean squared errorC.4Computer scienceBayesian probabilityG.3ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISIONInference02 engineering and technologyBayesian inferenceStatistics - Applications0202 electrical engineering electronic engineering information engineeringFOS: Electrical engineering electronic engineering information engineeringApplications (stat.AP)Electrical and Electronic EngineeringImage sensorI.4.1C.4; G.3; I.4.1Pixelbusiness.industryImage and Video Processing (eess.IV)020206 networking & telecommunicationsPattern recognitionStatistical modelElectrical Engineering and Systems Science - Image and Video ProcessingRandom effects modelNoise62P30 62P35 62F15 62J05Signal Processing020201 artificial intelligence & image processingComputer Vision and Pattern RecognitionArtificial intelligencebusinessSoftware
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Empirical analysis of daily cash flow time-series and its implications for forecasting

2019

Usual assumptions on the statistical properties of daily net cash flows include normality, absence of correlation and stationarity. We provide a comprehensive study based on a real-world cash flow data set showing that: (i) the usual assumption of normality, absence of correlation and stationarity hardly appear; (ii) non-linearity is often relevant for forecasting; and (iii) typical data transformations have little impact on linearity and normality. This evidence may lead to consider a more data-driven approach such as time-series forecasting in an attempt to provide cash managers with expert systems in cash management.

cash flowtime-serieseducationStatisticsforecasting:62 Statistics::62P Applications [Classificació AMS]62J02 62J05 62P20EconomiaNon-linearitynon-linearityCash flow:Matemàtiques i estadística::Estadística matemàtica [Àrees temàtiques de la UPC]:62 Statistics::62J Linear inference regression [Classificació AMS]Time-seriesStatistics forecasting cash flow non-linearity time-seriesForecasting
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Learning from Data to Speed-up Sorted Table Search Procedures: Methodology and Practical Guidelines

2020

Sorted Table Search Procedures are the quintessential query-answering tool, with widespread usage that now includes also Web Applications, e.g, Search Engines (Google Chrome) and ad Bidding Systems (AppNexus). Speeding them up, at very little cost in space, is still a quite significant achievement. Here we study to what extend Machine Learning Techniques can contribute to obtain such a speed-up via a systematic experimental comparison of known efficient implementations of Sorted Table Search procedures, with different Data Layouts, and their Learned counterparts developed here. We characterize the scenarios in which those latter can be profitably used with respect to the former, accounting …

FOS: Computer and information sciencesComputer Science - Machine LearningStatistics - Machine LearningComputer Science - Data Structures and AlgorithmsData Structures and Algorithms (cs.DS)Machine Learning (stat.ML)E.1; I.2.068T07 68P05 62J05 68P10E.1I.2.0Machine Learning (cs.LG)
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