Reports of the AAAI 2011 Fall Symposia
... and scalable inference mechanisms, more natural approaches to acquiring knowledge over extended periods, techniques that interface general-purpose reasoning with specialized methods for spatial and visual processing, and motivational mechanisms that influence reasoning and goal generation. Finally, ...
... and scalable inference mechanisms, more natural approaches to acquiring knowledge over extended periods, techniques that interface general-purpose reasoning with specialized methods for spatial and visual processing, and motivational mechanisms that influence reasoning and goal generation. Finally, ...
R basics
... • In plot: try to vary the following options - note that you can use several at once (and figure out what they do) type=“l”, “b” col= “hotpink” main=“important plot” type=“h” type=“S” These options are really arguments to the plot() function ...
... • In plot: try to vary the following options - note that you can use several at once (and figure out what they do) type=“l”, “b” col= “hotpink” main=“important plot” type=“h” type=“S” These options are really arguments to the plot() function ...
Functional volumes modeling: Scaling for group size
... Literature-derived FV models of the spatial distribution of M1-mouth loci were validated by comparisons to the data sets just described, as follows. Spatial probability distributions for SPI[1], SPI[2], SPI[5], and SPI[10] were computed (from the literature-derived, group-mean input data, above) and ...
... Literature-derived FV models of the spatial distribution of M1-mouth loci were validated by comparisons to the data sets just described, as follows. Spatial probability distributions for SPI[1], SPI[2], SPI[5], and SPI[10] were computed (from the literature-derived, group-mean input data, above) and ...
Towards Adversarial Reasoning in Statistical Relational Domains
... the search engine’s MAP labeling its maximum utility action. We can represent the spammer’s utility as the number of spam web pages that are not detected by the search engine, minus a penalty for the number of words and links modified in order to disguise these web pages (representing the effort or ...
... the search engine’s MAP labeling its maximum utility action. We can represent the spammer’s utility as the number of spam web pages that are not detected by the search engine, minus a penalty for the number of words and links modified in order to disguise these web pages (representing the effort or ...
PowerPoint slides
... The mean is defined to be the sum (S) of the data values divided by the total number of values (called n or N). We will compute two means: one for the sample (X) and one for a finite population of values (m). The mean, in most cases, is not an actual data value. © The McGraw-Hill Companies, Inc., 20 ...
... The mean is defined to be the sum (S) of the data values divided by the total number of values (called n or N). We will compute two means: one for the sample (X) and one for a finite population of values (m). The mean, in most cases, is not an actual data value. © The McGraw-Hill Companies, Inc., 20 ...
Differential Privacy for Social Science Inference
... Private or sensitive data may be loosely defined as data that, if known, can cause harm to an individual. Researchers who collect data on human subjects are often collecting sensitive data and require approval from their Institutional Review Board (IRB) to conduct their research. Once a dataset has ...
... Private or sensitive data may be loosely defined as data that, if known, can cause harm to an individual. Researchers who collect data on human subjects are often collecting sensitive data and require approval from their Institutional Review Board (IRB) to conduct their research. Once a dataset has ...
Logistic Regression - Virgil Zeigler-Hill
... Ratio of cases to variables – using discrete variables requires that there are enough responses in every given category ...
... Ratio of cases to variables – using discrete variables requires that there are enough responses in every given category ...
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.).