Advanced & Applied Topics · Computational Biostatistics & Genomics

Computational biostatistics: foundations and tools

Public introduction · full guide in Eikos

Introduction

What this guide covers

Introduction Introduction Algorithmic Mathematics · Life Sciences

Overview

Topics covered

  • Introduction
  • Advanced Computational Biostatistics and Modeling
  • Inference and Paradigms in Computational Biostatistics
  • Epistemological Divergences: Frequentist, Bayesian and Likelihood-Based Paradigms
  • Limits of Analytical Approaches and the Need for Computation
  • Stochastic Simulation and Generation of Random Variables

Related concepts

Main connections

  • Bayes' theorem & priors
  • Markov chain Monte Carlo (MCMC)
  • Maximum likelihood estimation
  • Multiple testing & FWER/FDR
  • Survival analysis & Kaplan-Meier
  • Bootstrap resampling

Compatible notation

Mathematical sample in LaTeX

The expression is rendered accessibly, with its LaTeX source preserved below.

π(θ∣X)=L(θ∣X)π(θ)∫ΘL(θ'∣X)π(θ')dθ'
View LaTeX source\pi(\theta \mid X) = \frac{L(\theta \mid X)\pi(\theta)}{\int_{\Theta} L(\theta' \mid X)\pi(\theta')d\theta'}