grisaitis/arduino-sketches
Public repository.
Developer Profile
grisaitis
Discovered public repositories for grisaitis in the GitHub catalog.
grisaitis/vagrant-profiles
Public repository.
grisaitis/learning-princeton-algs4
code from princeton's algs4 coursera course
grisaitis/ProgrammingAssignment2
Repository for Programming Assignment 2 for R Programming on Coursera
grisaitis/regressions
practicing implementations of common regression techniques
grisaitis/datasciencecoursera
Public repository.
grisaitis/sympy-sandbox
playing with sympy
grisaitis/javascriptLearning
Documenting my learning of JavaScript
grisaitis/bernoulli-montecarlo
Given a random process with only two possible outcomes and known chances of either outcome, what is the best way to predict the outcome? Perhaps obviously, the best method is simply always to predict the more probable outcome. But what about other methods, such as making a random guess each time according to the same probabilities of either outcome? In other words, if outcome "A" happens 60% of the time, then how often am I correct if I guess "A" only 60% of the time (and outcome "B" 40% of the time)? The answer is not 60%; indeed, it is less. Analytically, if Prob(outcome="A") = p, and you guess "A" only p*100% of the time, then you'll be correct only (p^2 + (1-p)^2) * 100% of the time. For any p other than 0.5, this method is less accurate than simply choosing "A" every time. This simple fact is demonstrated by this mini-monte carlo simulation.
grisaitis/PHY260
collection of all assignments for Duke University's Physics 260 "Computational Physics"
grisaitis/djangolesson
follows `djangoles-tutorial.pdf`
grisaitis/phuxvim
Public repository.