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Identifying and Accounting for Task-Dependent Bias in Crowdsourcing
Identifying and Accounting for Task-Dependent Bias in Crowdsourcing

Artificial Intelligence techniques: An introduction to their use for
Artificial Intelligence techniques: An introduction to their use for

... is available [41], and its application is quite limited. RBS are well suited to problems where experts can articulate decisions confidently and where variables interact little. They may be difficult to scale up, as interactions then emerge. Ecological systems, with complex interactions and processes ...
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1 How Bayesian statistics are needed to determine whether mental

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ArdiCh123F15

...  The median of a data set is the value in the middle when the data items are arranged in ascending order.  Whenever a data set has extreme values, the median is the preferred measure of central location.  The median is the measure of location most often reported for annual income and property val ...
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Analysis of EEG Signal for the Detection of Brain Abnormalities

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... Sets of objects, numbers, departments, job descriptions, etc. are things that we all deal with every day of our lives. Mathematical Set Theory just puts a structure around this concept so that sets can be used or manipulated in a logical way. The type of notation used is a reasonable and simple one. ...
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TI-83/84 Guide for Introductory Statistics

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Fulltext - Brunel University Research Archive

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A Taxonomy of Artificial Intelligence Approaches for Adaptive

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Considering Vertical and Horizontal Context in Corpus–based

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Decision Support Systems - University of Pittsburgh

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Introduction - Knowledge Based Systems Group

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1. BOOKS A random sample of 1000 U.S. college students is

... county; population: all high school students in the county; sample statistic: time spent each week on extracurricular activities by the sample; population 12-2parameter: Statistics and time Parameters spent each week on extracurricular activities by all students in the county 7. MONEY A stratified ...
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Temporal Firing Patterns of Purkinje Cells in the Cerebellar Ventral

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On Label Dependence in Multi

Matching Ottoman Words: An image retrieval approach to historical
Matching Ottoman Words: An image retrieval approach to historical

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Time series



A time series is a sequence of data points, typically consisting of successive measurements made over a time interval. Examples of time series are ocean tides, counts of sunspots, and the daily closing value of the Dow Jones Industrial Average. Time series are very frequently plotted via line charts. Time series are used in statistics, signal processing, pattern recognition, econometrics, mathematical finance, weather forecasting, intelligent transport and trajectory forecasting, earthquake prediction, electroencephalography, control engineering, astronomy, communications engineering, and largely in any domain of applied science and engineering which involves temporal measurements.Time series analysis comprises methods for analyzing time series data in order to extract meaningful statistics and other characteristics of the data. Time series forecasting is the use of a model to predict future values based on previously observed values. While regression analysis is often employed in such a way as to test theories that the current values of one or more independent time series affect the current value of another time series, this type of analysis of time series is not called ""time series analysis"", which focuses on comparing values of a single time series or multiple dependent time series at different points in time.Time series data have a natural temporal ordering. This makes time series analysis distinct from cross-sectional studies, in which there is no natural ordering of the observations (e.g. explaining people's wages by reference to their respective education levels, where the individuals' data could be entered in any order). Time series analysis is also distinct from spatial data analysis where the observations typically relate to geographical locations (e.g. accounting for house prices by the location as well as the intrinsic characteristics of the houses). A stochastic model for a time series will generally reflect the fact that observations close together in time will be more closely related than observations further apart. In addition, time series models will often make use of the natural one-way ordering of time so that values for a given period will be expressed as deriving in some way from past values, rather than from future values (see time reversibility.)Time series analysis can be applied to real-valued, continuous data, discrete numeric data, or discrete symbolic data (i.e. sequences of characters, such as letters and words in the English language.).
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