Notes
... Clustering of microarray data Plot each datum as a point in N-dimensional space Make a distance matrix for the distance between every two gene points in the N-dimensional space Genes with a small distance share the same expression characteristics and might be functionally related or similar. ...
... Clustering of microarray data Plot each datum as a point in N-dimensional space Make a distance matrix for the distance between every two gene points in the N-dimensional space Genes with a small distance share the same expression characteristics and might be functionally related or similar. ...
probability and statistics for engineers
... The origin of statistics can be traced to two areas of interest that, on the surface, have little in common: games of chance and what is now called political science. Mid-eighteenth century studies in probability, motivated largely by interest in games of chance, led to the mathematical treatment of ...
... The origin of statistics can be traced to two areas of interest that, on the surface, have little in common: games of chance and what is now called political science. Mid-eighteenth century studies in probability, motivated largely by interest in games of chance, led to the mathematical treatment of ...
Download File
... The definition of the variables selected and the justification for their selection, ...
... The definition of the variables selected and the justification for their selection, ...
ppt
... K-means clustering • Partitional clustering splits the data in k partitions with a given integer k. • Partition can represented by a partition matrix U that contains the membership values μij of each object i for each cluster j. • For clustering methods, which is based on classical set theory, clus ...
... K-means clustering • Partitional clustering splits the data in k partitions with a given integer k. • Partition can represented by a partition matrix U that contains the membership values μij of each object i for each cluster j. • For clustering methods, which is based on classical set theory, clus ...
portable document (.pdf) format
... the log GDP. According to AIC the AR(4) model is the best while BIC selects the AR(1) model. Figure 1 allows us to interpret the different models and to form our own opinion about their goodness. It shows the periodogram of the first differences together with the spectral densities of the estimated ...
... the log GDP. According to AIC the AR(4) model is the best while BIC selects the AR(1) model. Figure 1 allows us to interpret the different models and to form our own opinion about their goodness. It shows the periodogram of the first differences together with the spectral densities of the estimated ...
Exponential Family Distributions
... S. L. Lauritzen and N. Wermuth. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17:31–57, 1989. N. Lawrence, M. Milo, M. Niranjan, P. Rashbass, and S. Soullier. Reducing the variability in microarray image processing by ...
... S. L. Lauritzen and N. Wermuth. Graphical models for associations between variables, some of which are qualitative and some quantitative. Annals of Statistics, 17:31–57, 1989. N. Lawrence, M. Milo, M. Niranjan, P. Rashbass, and S. Soullier. Reducing the variability in microarray image processing by ...
Applying Description Logic to Product Behavioral Design within Advanced CAD Systems
... Traditional CAD systems represent mechanical objects by their topological and geometrical features. These properties however represent only a small part of the information needed during the product life cycle. Many proposals for integrating these constraints in the early stages of the design process ...
... Traditional CAD systems represent mechanical objects by their topological and geometrical features. These properties however represent only a small part of the information needed during the product life cycle. Many proposals for integrating these constraints in the early stages of the design process ...
The coming of age of artificial intelligence in medicine
... methods. With the diffusion of AI research throughout biomedical informatics, the biennial AIME conference, and the international journal Artificial Intelligence in Medicine stand out as the two remaining forces for defining and recognizing AI in medicine as a subfield of biomedical informatics and ...
... methods. With the diffusion of AI research throughout biomedical informatics, the biennial AIME conference, and the international journal Artificial Intelligence in Medicine stand out as the two remaining forces for defining and recognizing AI in medicine as a subfield of biomedical informatics and ...
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.).