Histogram and Bayesian modelling approach to describe empirical data

$30-250 SGD

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about 10 years ago

$30-250 SGD

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The Iris dataset consists of 150 samples of attributes of the Iris flowers from the following classes: Setosa, Virginica and Versicolor. Each class has 50 samples. The four attributes are Sepal Width (SW), Sepal Length (SL), Petal Width (PW) and Petal Length (PL). In this assignment, we will only consider the two classes of Virginica and Versicolor. Using 10 bins, quantize the data set into the joint histogram distribution for the dimension Petal Width (PW). Determine the joint probability distribution of attribute: PW for the two classes of Versicolor and Virginica. Determine the class apriori probabilities, conditional probabilities and posterior probabilities for each bin. Subsequently verify Bayes' Formula. Prepare the histogram, the joint probability distribution P(C, X) as well as the P(ri|C) and P(C|i) for even bins as a word doc.

Project ID: 5812925

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@ProDataAnalyzer

Hi, I am interested to the task. I have been a professional statistical analyst having MSc in Statistics. Please visit my public profile for details about me. As your requirements, I am able to graph the empirical data and find Bayesian probabilities. Please feel free to contact me directly to discuss this position further. Thanking you.

$320 SGD in 5 days