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smoothed data

См. также в других словарях:

  • Smoothed analysis — is a way of measuring the complexity of an algorithm. It gives a more realistic analysis of the practical performance of the algorithm, such as its running time, than using worst case or average case scenarios.For instance the simplex algorithm… …   Wikipedia

  • data compression — Process of reducing the amount of data needed for storage or transmission of a given piece of information (text, graphics, video, sound, etc.), typically by use of encoding techniques. Data compression is characterized as either lossy or lossless …   Universalium

  • Exponential smoothing — is a technique that can be applied to time series data, either to produce smoothed data for presentation, or to make forecasts. The time series data themselves are a sequence of observations. The observed phenomenon may be an essentially random… …   Wikipedia

  • Numerical smoothing and differentiation — An experimental datum value can be conceptually described as the sum of a signal and some noise, but in practice the two contributions cannot be separated. The purpose of smoothing is to increase the Signal to noise ratio without greatly… …   Wikipedia

  • Demarker Indicator — An indicator used in technical analysis that compares the most recent price action to the previous period s price in an attempt to measure the demand of the underlying asset. This indicator is generally used to identify price exhaustion and can… …   Investment dictionary

  • Multivariate kernel density estimation — Kernel density estimation is a nonparametric technique for density estimation i.e., estimation of probability density functions, which is one of the fundamental questions in statistics. It can be viewed as a generalisation of histogram density… …   Wikipedia

  • Unicode font — A Unicode font (also known as UCS font and Unicode typeface) is a computer font that contains a wide range of characters, letters, digits, glyphs, symbols, ideograms, logograms, etc., which are collectively mapped into the standard Universal… …   Wikipedia

  • Bootstrapping (statistics) — In statistics, bootstrapping is a modern, computer intensive, general purpose approach to statistical inference, falling within a broader class of resampling methods.Bootstrapping is the practice of estimating properties of an estimator (such as… …   Wikipedia

  • Kernel density estimation — of 100 normally distributed random numbers using different smoothing bandwidths. In statistics, kernel density estimation is a non parametric way of estimating the probability density function of a random variable. Kernel density estimation is a… …   Wikipedia

  • Local regression — LOESS, or locally weighted scatterplot smoothing, is one of many modern modeling methods that build on classical methods, such as linear and nonlinear least squares regression. Modern regression methods are designed to address situations in which …   Wikipedia

  • k-means clustering — In statistics and data mining, k means clustering is a method of cluster analysis which aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean. This results into a partitioning of… …   Wikipedia

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