A Markov Chain is a random process, where we assume the previous state(s) hold sufficient predictive power in predicting the next state. Unlike flipping a coin, these events are dependent. It’s easier to understand through an example.…

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Posted on August 23, 2015 at 4:30pm 0 Comments 0 Likes

Most people (including myself) are drawn to Julia by its lofty goals. Speed of C, statistical packages of R, and ease of Python?—it sounds two good to be true. However, I haven't seen anyone who has looked into it say the developers behind the language aren't on track to accomplish these goals.…

ContinuePosted on August 4, 2015 at 8:00pm 0 Comments 0 Likes

A Markov Chain is a random process, where we assume the previous state(s) hold sufficient predictive power in predicting the next state. Unlike flipping a coin, these events are dependent. It’s easier to understand through an example.…

Posted on July 25, 2015 at 6:00pm 5 Comments 0 Likes

I’m going to keep this tutorial light on math, because the goal is just to give a general understanding.

The idea of Monte Carlo methods is this—*generate some random samples for some random variable of interest, then use these samples to compute values you’re interested in*.

I know, super broad. The truth is Monte Carlo has a ton of different applications. It’s…

ContinuePosted on July 19, 2015 at 8:31am 1 Comment 0 Likes

Linear regression is one of the first things you should try if you’re modeling a linear relationship (actually, non-linear relationships too!). It’s fairly simple, and probably the first thing to learn when tackling machine learning.

At first, linear regression shows up just as a simple equation for a line. In machine learning, the weights are usually represented by a vector θ (in statistics they’re often represented…

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Hi Alex,

You might enjoy some meanderings into quasi-random point generation here:

http://learningandotherthings.blogspot.com/2015/07/anti-clustering.html and other little topics in my blog.

Cheers,

John