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.…

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Alex Woods's blog post was featured### Linear Algebra in Julia

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.Having only been around since 2012, Julia's greatest disadvantage is a lack of community support. If you have an obscure Julia…See More

Aug 25, 2015

John MacCuish left a comment for Alex Woods

"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"

Aug 5, 2015

Alex Woods's blog post was featured### Generating Text Using a Markov Model

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.Imagine the weather can only be rainy or sunny. That is, the state space is…See More

Aug 5, 2015

Vincent Granville commented on Alex Woods's blog post Introduction to Monte Carlo Methods

"Alex, what I have in mind is to create synthetic numbers that have fast/efficient series expansions (like Pi) or basic recurrence formulas (like SRT{n} for small n), to generate/reproduce the randomness that these famous numbers exhibit. My…"

Aug 4, 2015

John MacCuish commented on Alex Woods's blog post Introduction to Monte Carlo Methods

"Alex, It has to do with low discrepancy sets in small dimensions. See https://en.wikipedia.org/wiki/Quasi-Monte_Carlo_method
They show how to mix pseudo and quasi to generate sets that perform well in higher dimensions.
A great book on monte…"

Aug 4, 2015

Alex Woods commented on Alex Woods's blog post Introduction to Monte Carlo Methods

"Vincent - Very cool. It seems like any of the doubts I had about it you addressed with the tests (in your article). Definitely enjoyed that article.
John - Great suggestion! I tried it; it worked. Why?"

Aug 4, 2015

Vincent Granville commented on Alex Woods's blog post Introduction to Monte Carlo Methods

"I also wrote a bit about random generators, see here. My interest is in using decimals of some irrational numbers (Pi has an easy, fast formula to generate decimals, I'm working on a formula for SQRT{2}), as well as synthetic numbers designed…"

Aug 1, 2015

John MacCuish commented on Alex Woods's blog post Introduction to Monte Carlo Methods

"The R randtoolbox package has a nice set of quasi-random sequence generators, such that if you substitute, say, the "halton" function for "runif" in your code, you will see that the error converges far more quickly. For…"

Jul 31, 2015

Alex Woods commented on Alex Woods's blog post Guide To Linear Regression

"Note for those reading thoroughly - the second to last equation should be Y ~ ... , not Y = ..."

Jul 21, 2015

Alex Woods's blog post was featured### Guide To Linear Regression

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 by A and B!).But then we have to account for more than just one input variable.…See More

Jul 20, 2015

Alex Woods's 2 blog posts were featured

Jul 7, 2015

Alex Woods commented on Jennifer Methvin's blog post Data scientists are wasting their time

"Data wrangling takes such a high proportion of time because the machine learning algorithms are coded up for you, in neat packages like scikit-learn. This is a great thing, and it makes data science accessible to people who don't have a…"

Jul 4, 2015

- Short Bio:
- Student of in machine learning.

- My Website or LinkedIn Profile (URL):
- http://alexhwoods.com

- Field of Expertise:
- Business Analytics, Data Mining, Statistical Programming, Artificial Intelligence

- Professional Status:
- Student, Technical

- Interests:
- Networking, New Venture

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.…

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…

Continue- At 4:06pm on August 5, 2015, John MacCuish said…
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

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