The place where statistics, coffee and bayes theorem come toghether

Matías Castillo-Aguilar

Introduction

Let’s be honest, being a statistician or a scientist

 isn’t just about crunching numbers all day

. It’s more like being a detective, a problem solver, and yeah, throwing in some math for good measure. When we get into

 non-linear models

, things really start to get interesting. We’re not just drawing straight lines anymore;

Non-linear models: Pharmacokinetics and Indomethacin

Hello stranger

First of all, welcome to the first post of “Bayesically Speaking” (which, in case you haven’t noticed, is a word play between “Basically Speaking” and the (hopefully) well-known Bayes’ theorem), and although the web is offline at the time of writing this article, I find myself following the advice of all those people who encouraged me to trust my instinct and dare to do what I have always wanted: to be able to

Welcome to Bayesically Speaking

Introduction

Hey there, fellow science enthusiasts and stats geeks! Welcome back to the wild world of Markov Chain Monte Carlo (MCMC) algorithms. This is

 part two

of my series on the

 powerhouse behind Bayesian Inference

. If you missed the first post, no worries! Just hop on over here and catch up before we dive deeper into the MCMC madness.

The Good, The Bad, and Hamiltonian Monte Carlo

Introduction

Alright, folks, let’s dive into the wild world of statistics and data science! Picture this: you’re knee-deep in data, trying to make sense of the chaos.

Bayesically Speaking

Markov Chain Monte What?