biostats - CMU Philosophy Department Web Server
... - latent common causes of pairs in X U Y: T = {T1, …, Tk} • Let the true causal model over V be a Structural Equation Model in which each V V is a linear combination of its direct causes and independent, Gaussian noise. Nov. 13th, 2003 ...
... - latent common causes of pairs in X U Y: T = {T1, …, Tk} • Let the true causal model over V be a Structural Equation Model in which each V V is a linear combination of its direct causes and independent, Gaussian noise. Nov. 13th, 2003 ...
The Promise of Artificial Intelligence
... consumers with their holiday shopping, to accelerating the process of discovering new lifesaving drugs.23 Most uses of AI have at least one of seven functions: monitoring; discovering; predicting; interpreting; interacting with the physical environment; interacting with humans; and interacting with ...
... consumers with their holiday shopping, to accelerating the process of discovering new lifesaving drugs.23 Most uses of AI have at least one of seven functions: monitoring; discovering; predicting; interpreting; interacting with the physical environment; interacting with humans; and interacting with ...
Na¨ıve Inference viewed as Computation
... conditional assertions. Application of conditional probabilities to relevant unconditional values has the potential to identify new unconditional values. These can then be the basis for production of further values, and so on, in a potentially infinite sequence. Naı̈ve inference becomes the behaviou ...
... conditional assertions. Application of conditional probabilities to relevant unconditional values has the potential to identify new unconditional values. These can then be the basis for production of further values, and so on, in a potentially infinite sequence. Naı̈ve inference becomes the behaviou ...
Practical and Effective Approaches to Dealing with Clustered Data
... Unfortunately, evidence reveals a major problem with using CRSEs in datasets that have a small number of clusters: using CRSEs when the number of clusters is small can cause models to find statistically significant relationships where no relationships actually exist. That is, when only a small numb ...
... Unfortunately, evidence reveals a major problem with using CRSEs in datasets that have a small number of clusters: using CRSEs when the number of clusters is small can cause models to find statistically significant relationships where no relationships actually exist. That is, when only a small numb ...
Corpus-based, Statistical Goal Recognition
... through the input with 83.9% overall accuracy. This early prediction is crucial to our domain of dialogue systems. Most work does not report how early the recognizer makes correct predictions. Lesh [1998] simulates a task-completion agent, which, upon recognizing the user’s goal, steps in to complet ...
... through the input with 83.9% overall accuracy. This early prediction is crucial to our domain of dialogue systems. Most work does not report how early the recognizer makes correct predictions. Lesh [1998] simulates a task-completion agent, which, upon recognizing the user’s goal, steps in to complet ...
Binary Dependent Variables
... of the response being 1 as a nonlinear function of linear combinations of explanatory variables. • To accommodate heterogeneity, we incorporate subjectspecific variables of the form: pit = (i + xit ). – Here, the subject-specific effects account only for the intercepts and do not include other ...
... of the response being 1 as a nonlinear function of linear combinations of explanatory variables. • To accommodate heterogeneity, we incorporate subjectspecific variables of the form: pit = (i + xit ). – Here, the subject-specific effects account only for the intercepts and do not include other ...
Binary Dependent Variables
... of the response being 1 as a nonlinear function of linear combinations of explanatory variables. • To accommodate heterogeneity, we incorporate subjectspecific variables of the form: pit = (i + xit ). – Here, the subject-specific effects account only for the intercepts and do not include other ...
... of the response being 1 as a nonlinear function of linear combinations of explanatory variables. • To accommodate heterogeneity, we incorporate subjectspecific variables of the form: pit = (i + xit ). – Here, the subject-specific effects account only for the intercepts and do not include other ...
Discriminative Improvements to Distributional Sentence Similarity
... both training and (unlabeled) test data can be viewed as a form of transductive learning (Gammerman et al., 1998), where we assume access to unlabeled test set instances.2 We also consider an inductive setting, where we construct the basis of the latent space from only the training set, and then pro ...
... both training and (unlabeled) test data can be viewed as a form of transductive learning (Gammerman et al., 1998), where we assume access to unlabeled test set instances.2 We also consider an inductive setting, where we construct the basis of the latent space from only the training set, and then pro ...
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.).