General Longitudinal Modeling of Individual Differences in
... to present some relevant power results for longitudinal modeling in intervention studies. This article uses the general latent variable framework to consider power estimation using a method developed for latent variable models by Satorra and Saris (1985). Although the article focuses on multiple-pop ...
... to present some relevant power results for longitudinal modeling in intervention studies. This article uses the general latent variable framework to consider power estimation using a method developed for latent variable models by Satorra and Saris (1985). Although the article focuses on multiple-pop ...
Probabilistic ODE Solvers with Runge-Kutta Means
... Calibration of uncertainty A question easily posed but hard to answer is what it means for the probability distribution returned by a probabilistic method to be well calibrated. For our Gaussian case, requiring RK order in the posterior mean determines all but one degree of freedom of an answer. The ...
... Calibration of uncertainty A question easily posed but hard to answer is what it means for the probability distribution returned by a probabilistic method to be well calibrated. For our Gaussian case, requiring RK order in the posterior mean determines all but one degree of freedom of an answer. The ...
AutoLeadGuitar: Automatic Generation of Guitar Solo Phrases in the
... and compositional model, which are evaluated in Section IV. Finally, we conclude the work and discuss areas of future research in Section V. ...
... and compositional model, which are evaluated in Section IV. Finally, we conclude the work and discuss areas of future research in Section V. ...
Gaussian processes
... extremely high-dimensional vector drawn from an extremely high-dimensional multivariate Gaussian. Here, each dimension of the Gaussian corresponds to an element x from the index set X , and the corresponding component of the random vector represents the value of f (x). Using the marginalization prop ...
... extremely high-dimensional vector drawn from an extremely high-dimensional multivariate Gaussian. Here, each dimension of the Gaussian corresponds to an element x from the index set X , and the corresponding component of the random vector represents the value of f (x). Using the marginalization prop ...
Hybrid Inductive Machine Learning: An Overview of CLIP Algorithms
... where the clusters in a given data set are found, and characterization where, for each found cluster, a concept description is generated. Conceptual clustering can be thought of as a hybrid of unsupervised (clustering) and supervised (characterization) learning. In theory, it is possible to transfor ...
... where the clusters in a given data set are found, and characterization where, for each found cluster, a concept description is generated. Conceptual clustering can be thought of as a hybrid of unsupervised (clustering) and supervised (characterization) learning. In theory, it is possible to transfor ...
Workshops Held at the First AAAI Conference on Human
... and user studies paradigms. Through crowdsourcing, research can scale and allow more robust models, better results, more accurate assessments and conclusions, and more sophisticated user studies. This workshop brought to light some of the most recent research and findings in the use of the crowdsour ...
... and user studies paradigms. Through crowdsourcing, research can scale and allow more robust models, better results, more accurate assessments and conclusions, and more sophisticated user studies. This workshop brought to light some of the most recent research and findings in the use of the crowdsour ...
Intelligent Automation
... data, is particularly popular – show a computer enough pictures of cats, and at some point it will recognize most cats. But there are other approaches as well: teaching machines to make decisions based on probability, utility or causality, for instance. The point is that ‘artificial’ intelligence is ...
... data, is particularly popular – show a computer enough pictures of cats, and at some point it will recognize most cats. But there are other approaches as well: teaching machines to make decisions based on probability, utility or causality, for instance. The point is that ‘artificial’ intelligence is ...
Chap. 2: Methods for Describing Sets of Data
... will have a z-score between –1 and 1. 2. Approximately 95% of the measurements will have a z-score between –2 and 2. 3. Approximately 99.7% of the measurements will have a z-score between –3 and 3. (see the figure on the next slide) © 2011 Pearson Education, Inc ...
... will have a z-score between –1 and 1. 2. Approximately 95% of the measurements will have a z-score between –2 and 2. 3. Approximately 99.7% of the measurements will have a z-score between –3 and 3. (see the figure on the next slide) © 2011 Pearson Education, Inc ...
Possibilistic conditional independence: A similarity
... on the resulting D A G to guide the K2 algorithm [47]. For a wider and more detailed discussion of current network learning methods see [41]. All these methods have been applied using a single uncertainty formalism, i.e., probability. However, uncertainty about a domain can be due to other factors b ...
... on the resulting D A G to guide the K2 algorithm [47]. For a wider and more detailed discussion of current network learning methods see [41]. All these methods have been applied using a single uncertainty formalism, i.e., probability. However, uncertainty about a domain can be due to other factors b ...
ppt
... formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data. Labeled examples: training & testing data Admissible rules (hypotheses space) Search strategy ...
... formal rules are extracted from a set of observations. The rules extracted may represent a full scientific model of the data, or merely represent local patterns in the data. Labeled examples: training & testing data Admissible rules (hypotheses space) Search strategy ...
Improving the Knowledge-Based Expert System Lifecycle
... Traditional knowledge-based expert system implementations do not utilize the information contained in these historical data repositories even though these repositories contain valuable information that could be mined into the knowledge base for future use. 1.3 Issues Associated with Knowledge Base M ...
... Traditional knowledge-based expert system implementations do not utilize the information contained in these historical data repositories even though these repositories contain valuable information that could be mined into the knowledge base for future use. 1.3 Issues Associated with Knowledge Base M ...
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.).