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Full explanations on Basic Bayes Probability, the terminology used, and the default example of this program are provided in the Introduction and Basic Bayes panels of Classification by Bayes Probability Explained Page
. This panel presents brief summaries, sufficient only to help new users to negotiate data entry and program options.
Default ExampleThe data in the default example are artificially generated and does not represent reality. It purports to be from an exercise to use hair color (Dark and Light) and eye color (Blue, Brown, Others) to identify ethnicity (French, German, Italian). Once the model is established, it is used in a population where the ratios of French:German:Italians are 3:2:1 (normalized to 0.5:0.3333:0.1667).InputPredictor is what we use to predict outcome. The Basic Bayes model has only 1 predictor. When there are more than 1 variables used to predict, they must be combined into a single compound predictor. In our example, hair and eye colors are comined to form a compound predictor HairEyeColor Attributes (a) are mutually exclusive alternatives in the predictor. In a compound predictor, the number of attributes is the multiple of attribute numbers from each original predictor. In our example, 2 hair and 3 eye color are combined into 2x3=6 attributes of DarkBlue, DarkBrown, DarkOthers, LightBlue, LightBrown and LightOthers. Input Data are placed in the first text area labeled "Data" in Program 1, and can be in one or two formats
a priori Probability (π) is a believe in the probability of belonging to an outcome prior to executing the Baysian model. In data entry, any set of number representing relative proportions (sample size, ratios, percents) can be used, and the program will normalize them by dividing each by the total. The default example assigned the ratio of French:German:Italian to 3:2:1, normalized to 0.5:0.3333:0.1667. Progam OutputProbabilitiesDuring model development, the coefficient produced from the reference data is the Probability of attribute (a) given outcome (o), P(a|o). When using P(a|o) to predict, however, the following probabilities can be produced
Javascript Program produces a function that will match a text string against the reference attributes, and returns the appropriate array of probabilities. The P(a|o) table must be available in the text area in Program 3, and the program will also read the a priori array in their textboxes near Program 1. The script produced can be copied and pasted into a html page and useed to produce probabilities. BasicBayesTemplate.html is a template html page demonstrating how the function produced by this page using default example data. ProgramsThe programs on this page consists of a single program with 3 points of entry (Program 1-3), and a supplementary program (program 4) to create a Javascript interpreter. The Example buttons triggers the loading of the default example data for each entry, and runs the program using that data. Users can enter his/her own data and runs the program with the program buttonsProgram 1 produces prediction model using raw data, and interprets the same data
Program 2 produces the coefficients P(a|o) using a table of counts. On clicking the Example or Analyse Reference Table button, or if Program 1 is already running, the program will
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