Using the Monte Carlo techniques I have described in earlier posts, I ran several simulations today to try to predict who will win the 2016 Michigan primaries. Here is what I found:
For the Republican primaries, I predict:
Trump: 89.64% chance of winning
Cruz: 5.01% chance of winning
Kasich: 3.29% chance of winning
Rubio: 2.06% chance of winning
The following plot is a histogram of the simulations:
A great deal of noise has been made in the previous weeks about the surge in the polls of Donald Trump and Bernie Sanders. This has led some people to question whether Hillary Clinton will actually end up being the Democratic party nominee in 2016. This was further evidenced by the fact that Sanders is now leading Clinton in the latest New Hampshire polls.
However, running an analysis on current polling data, I still believe that even though it is very early, Hillary Clinton still has the best chance of being the Democratic party nominee. In fact, running some algorithms against the current data, I found that:
Hillary Clinton: chance of winning Democratic nomination.
Bernie Sanders: chance of winning Democratic nomination.
These numbers were deduced from an algorithm that used non-parametric methods to obtain the following probability density functions.
Thanks to Hargun Singh Kohli for data compilation and research.