Probabilistic Graphical Models: Bayesian Networks Example With R, Python, SAMIAM

Here I try to practice examples of Bayesian networks as explained in the book written by Stanford Professor Daphne Koller, and Hebrew Professor Nir Friedman., “Probabilistic Graphical Models: Principle and Techniques”, chapter 3.2.   

In the book, the final values of probabilities is given as it is, without derivation of the formulas. Here I tried to expand the formulas, as well to verify the results with the help of R, Python and SamIam.

Course: PGM Specialization (MATLAB / Octave). 
Tools: R, Python, SamIam.  

The DAG of student BN example is, 

 

Handling US NOAA Storm Database's Exponent Value of PROPDMGEXP and CROPDMGEXP

How To Handle Exponent Value of PROPDMGEXP and CROPDMGEXP of "StormData.csv"

 

Reproducible Research Project 2, Coursera, Johns Hopkins University

U.S. National Oceanic and Atmospheric Administration’s (NOAA) Storm Database

There is confusion on how to handle exponent value of PROPDMGEXP and CROPDMGEXP columns of the database. Due to lack of official information in the NOAA website.

This is an attempt to compare downloaded database with the online version, to find conclusion what is meaning of each value actually.

This analysis is inspired by a post made by David Hood, himself is CTA in the Data Science Specialization courses.

At the end of this article, there is more accurate analysis done by Eddie Song.   

GitHub PDF and Markdown repository:
 https://github.com/flyingdisc/RepData_PeerAssessment
Rpubs: http://rpubs.com/flyingdisc/PROPDMGEXP
Reproducible report of the project: http://rpubs.com/flyingdisc/RepProject2