Selectivity Estimation using Probabilistic Models
... select * from Person, Purchase where Person.id = Purchase.buyer-id and Person.Income = high and Purchase.Type=luxury School ...
... select * from Person, Purchase where Person.id = Purchase.buyer-id and Person.Income = high and Purchase.Type=luxury School ...
The Posterior Distribution
... Again, with these data, the posterior distribution is a linear ramp from 0 to 1. Some probability exists for b<1, yet the MAP picks out the maximum at 1. ...
... Again, with these data, the posterior distribution is a linear ramp from 0 to 1. Some probability exists for b<1, yet the MAP picks out the maximum at 1. ...
Association Rule Mining based on Apriori Algorithm in
... wherein the input file is converted into numerical data and the transaction file is compressed into an array where further processing is done. ...
... wherein the input file is converted into numerical data and the transaction file is compressed into an array where further processing is done. ...
STATISTICS WITH SPREADSHEETS What is a spreadsheet? A
... data in a grid of multiple cells. Its primary purposes are to display data and aid in its analysis. The most popular spreadsheet application is Miscrosoft Excel. Your computer most likely has it (if you have Microsoft Word on your computer, the likelihood is even greater). If you don’t have access t ...
... data in a grid of multiple cells. Its primary purposes are to display data and aid in its analysis. The most popular spreadsheet application is Miscrosoft Excel. Your computer most likely has it (if you have Microsoft Word on your computer, the likelihood is even greater). If you don’t have access t ...
Statistics 2014, Fall 2001
... Sometimes we have several predictors, and one or more of them is only weakly related to the response variable. After including some of the stronger predictors in the model, we want to know whether it would make sense to include any of the weaker predictors as well. Anytime we include another predict ...
... Sometimes we have several predictors, and one or more of them is only weakly related to the response variable. After including some of the stronger predictors in the model, we want to know whether it would make sense to include any of the weaker predictors as well. Anytime we include another predict ...
Determining Optimal Parameters in Magnetic
... The attitude control of a spacecraft that uses magnetorquers as torque actuators is a very important task in astronautics. Many control laws have been designed for this task. A survey of various approaches is in [6]; in particular [1] proposes a feedback control law that, besides measures of the geo ...
... The attitude control of a spacecraft that uses magnetorquers as torque actuators is a very important task in astronautics. Many control laws have been designed for this task. A survey of various approaches is in [6]; in particular [1] proposes a feedback control law that, besides measures of the geo ...
From n00b to Pro
... • Should include everything needed to generate the data several times, find a linear model, and extract the coefficients • Does NOT take any inputs • Should return the matrix of 10,000 runs of coefficients ...
... • Should include everything needed to generate the data several times, find a linear model, and extract the coefficients • Does NOT take any inputs • Should return the matrix of 10,000 runs of coefficients ...
Document
... of model’s performance due to overfitting. • Training data set - train a range of models, or a given model with a range of values for its parameters. • Compare them on independent data – Validation set. – If the model design is iterated many times, then some overfitting to the validation data can oc ...
... of model’s performance due to overfitting. • Training data set - train a range of models, or a given model with a range of values for its parameters. • Compare them on independent data – Validation set. – If the model design is iterated many times, then some overfitting to the validation data can oc ...
Data Mining - Clustering
... clustering of very large databases. It automatically determines the number of clusters to be generated. Similarities between records are determined by comparing their field values. The clusters are then defined so that Condorcet’s criterion is maximised: (sum of all record similarities of pairs in t ...
... clustering of very large databases. It automatically determines the number of clusters to be generated. Similarities between records are determined by comparing their field values. The clusters are then defined so that Condorcet’s criterion is maximised: (sum of all record similarities of pairs in t ...
Expectation–maximization algorithm
In statistics, an expectation–maximization (EM) algorithm is an iterative method for finding maximum likelihood or maximum a posteriori (MAP) estimates of parameters in statistical models, where the model depends on unobserved latent variables. The EM iteration alternates between performing an expectation (E) step, which creates a function for the expectation of the log-likelihood evaluated using the current estimate for the parameters, and a maximization (M) step, which computes parameters maximizing the expected log-likelihood found on the E step. These parameter-estimates are then used to determine the distribution of the latent variables in the next E step.