In this post, I'm going to write about how the ever versatile normal distribution can be used to approximate a Bayesian posterior distribution. Unlike some other normal approximations, this is not a direct application of the central limit theorem. The result has a straight forward proof using Laplace's Method whose main ideas I will attempt to present. I'll also simulate a simple scenario to see how it works in practice.
This post is going to look at a useful non-parametric method for estimating the cumulative distribution function (CDF) of a random variable called the empirical distribution function (sometimes called the empirical CDF). We'll talk a bit about the mechanics of computing it, some theory about its confidence intervals and also do some simulations to gain some intuition about how it behaves.
This post is going to look at some elementary statistics for direct marketing. Most of the techniques are direct applications of topics learned in a first year statistics course hence the "elementary". I'll start off by covering some background and terminology on the direct marketing and then introduce some of the statistical inference techniques that are commonly used. As usual, I'll mix in some theory where appropriate to build some intuition.
This post is about some fundamental concepts in classical (or frequentist) statistics: inference and hypothesis testing. A while back, I came to the realization that I didn't have a good intuition of these concepts (at least not to my liking) beyond the mechanical nature of applying them. What was missing was how they related to a probabilistic view of the subject. This bothered me since having a good intuition about a subject is probably the most useful (and fun!) part of learning a subject. So this post is a result of my re-education on these topics. Enjoy!
In this post, I'm going to continue on the same theme from the last post: random sampling. We're going to look at two methods for sampling a distribution: rejection sampling and Markov Chain Monte Carlo Methods (MCMC) using the Metropolis Hastings algorithm. As usual, I'll be providing a mix of intuitive explanations, theory and some examples with code. Hopefully, this will help explain a relatively straight-forward topic that is frequently presented in a complex way.