Dr. Juan Camilo Orduz

Motivated by the nice talk on Winning with Simple, even Linear, Models by Vincent D.

Updated Version: 2019/09/21 (Extension + Minor Corrections) After a sequence of preliminary posts (Sampling from a Multivariate Normal Distribution and Regularized Bayesian Regression as a Gaussian Process), I want to explore a concrete example of a gauss…

In this post we study the Bayesian Regression model to explore and compare the weight and function space and views of Gaussian Process Regression as described in the book Gaussian Processes for Machine Learning, Ch 2.

In this post I want to describe how to sample from a multivariate normal distribution following section A.2 Gaussian Identities of the book Gaussian Processes for Machine Learning.

In this post I want to describe how to dockerize a simple Shiny App.

When working in data analysis it is almost impossible to avoid using linear algebra, even if it is on the background, e.g. simple linear regression.

In this post I show some text generation experiments I ran using LSTM with Keras.

I continue exploring the curse of dimensionality.

Nowadays social media generates a vast amount of raw data (text, images, videos, etc).

In this post I want to present the notion of curse of dimensionality following a suggested exercise (Chapter 4 - Ex. 4) of the book An Introduction to Statistical Learning, written by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.

Here I want to discuss my transition from Pelican to Blogdown and present some personal learnings.

In this expository article I discuss the definition and basic properties of the Hopf fibration, with particular emphasis on Dirac-type operators induced, in the sense of Brüning and Heintze, by the Hodge-de Rham and spin-Dirac operators.

In this post we present a simple example of how to expose a prediction model to a web API using the Plumber package.

We explore how to include an R notebook into a pelican post.

We give an introduction to PyMC3, a probabilistic programming framework written in Python.

This post contains the slides and material from a talk I gave at PyData Berlin 2018.

We study the problem of computing the probability that a given observation is part of a bootstrap sample.

I present the content of my PhD Thesis in mathematics, which has now been published in The Journal of Geometric Analysis.

We give an introduction to PyMC3, a probabilistic programming framework written in Python.

We describe how to use Beautiful Soup to scrape the official goverment website in order to get the results of the peace referendum in Colombia.

The objective of this post is to explore MathJax, a JavaScript display engine for LaTeX.

In this first post we get started with a small python script to explore the basic capabilities of Pelican.