|
You are here |
ebtech.github.io | ||
| | | | |
worldisnotenough.org
|
|
| | | | | The risks of dictatorship in America are real, but shouldn't be overstated. | |
| | | | |
mathematicaloddsandends.wordpress.com
|
|
| | | | | I recently learned of Craig's formula for the Gaussian Q-function from this blog post from John Cook. Here is the formula: Proposition (Craig's formula). Let $latex Z$ be a standard normal random variable. Then for any $latex z \geq 0$, defining $latex \begin{aligned} \mathbb{P}\{ Z \geq z\} = Q(z) = \dfrac{1}{\sqrt{2\pi}} \int_z^\infty \exp \left( -... | |
| | | | |
poissonisfish.com
|
|
| | | | | Someof the most fundamental functions in R, in my opinion, are those that deal with probability distributions. Whenever you compute a P-value you relyon a probability distribution, and there are many types out there. In this exercise I will cover four: Bernoulli, Binomial, Poisson, and Normal distributions. Let me begin with some theory first: Bernoulli... | |
| | | | |
teddykoker.com
|
|
| | | A few posts back I wrote about a common parameter optimization method known as Gradient Ascent. In this post we will see how a similar method can be used to create a model that can classify data. This time, instead of using gradient ascent to maximize a reward function, we will use gradient descent to minimize a cost function. Lets start by importing all the libraries we need: | ||