Vaccine efficacies (with COVID-19 examples) - Bayesian posterior VE/CI calculations
It was interesting to see that Pfizer/BioNTech used a Bayesian analysis of the primary endpoint for its COVID-19 vaccine. There are a few posts on this alrea...
Posts by tag
- Bayesian 4
- Monte Carlo methods 3
- Statistical inference 3
- Deployment 2
- Stochastic processes 1
- Models 1
- Clinical trials 1
- Model explainability 1
- Meta-learning 1
Bayesian
Gibbs sampling of multivariate probability distributions
This is a continuation of a previous article I have written on Bayesian inference using Markov chain Monte Carlo (MCMC). Here we will extend to multivariate ...
Bayesian inference using Markov Chain Monte Carlo with Python (from scratch and with PyMC3)
A guide to Bayesian inference using Markov Chain Monte Carlo (Metropolis-Hastings algorithm) with python examples, and exploration of different data size/par...
Getting started with Gaussian process regression modeling
Gaussian processing (GP) is quite a useful technique that enables a non-parametric Bayesian approach to modeling. It has wide applicability in areas such as ...
Monte Carlo methods
Gibbs sampling of multivariate probability distributions
This is a continuation of a previous article I have written on Bayesian inference using Markov chain Monte Carlo (MCMC). Here we will extend to multivariate ...
Bayesian inference using Markov Chain Monte Carlo with Python (from scratch and with PyMC3)
A guide to Bayesian inference using Markov Chain Monte Carlo (Metropolis-Hastings algorithm) with python examples, and exploration of different data size/par...
Monte Carlo integration in Python over univariate and multivariate functions
Monte Carlo integration is a basic Monte Carlo method for numerically estimating the integration of a function \(f(x)\). We will discuss here the theory alon...
Statistical inference
Expectation-maximization in general and for Gaussian mixtures
In statistical inference, we want to find what is the best model parameters given the observed data. In the frequentist view, this is about maximizing the li...
Gibbs sampling of multivariate probability distributions
This is a continuation of a previous article I have written on Bayesian inference using Markov chain Monte Carlo (MCMC). Here we will extend to multivariate ...
Bayesian inference using Markov Chain Monte Carlo with Python (from scratch and with PyMC3)
A guide to Bayesian inference using Markov Chain Monte Carlo (Metropolis-Hastings algorithm) with python examples, and exploration of different data size/par...
Deployment
Using AWS Lambda functions in VPC with VPC endpoints
AWS Lambda is a great way to create a serverless event-driven compute service. There are a few set-ups headaches when it comes to having the Lambda function ...
Quick start on deploying your Dash application using Heroku on a custom domain
Platform as a service (Paas) and authentication platforms are making application deployments easier than before. Rather than shipping your python Dash applic...
Stochastic processes
Getting started with Gaussian process regression modeling
Gaussian processing (GP) is quite a useful technique that enables a non-parametric Bayesian approach to modeling. It has wide applicability in areas such as ...
Models
Getting started with Gaussian process regression modeling
Gaussian processing (GP) is quite a useful technique that enables a non-parametric Bayesian approach to modeling. It has wide applicability in areas such as ...
Clinical trials
Vaccine efficacies (with COVID-19 examples) - Bayesian posterior VE/CI calculations
It was interesting to see that Pfizer/BioNTech used a Bayesian analysis of the primary endpoint for its COVID-19 vaccine. There are a few posts on this alrea...
Model explainability
Understanding binary classifier model structure based on Shapley feature interaction patterns
I have written a full version of this article, available on bioRxiv. A copy of which is presented below.
Meta-learning
Basics of few-shot learning with optimization-based meta-learning
Many machine learning models (particularly deep neural nets) require extensive training data. The idea of few-shot learning is to find ways to build models t...