Last week was the Plantae Seminar "Computational Plant Science - Science at the Interface of Math, Computer Science, and Plant Biology with Alexander Bucksch": Dr. Bucksch mentions using two clustering techniques - B splines and K-means clustering. I've discussed K-means clustering in a previous post to analyze predictors of success in Settlers of Catan. B splines… Continue reading Computational Plant Science – Clustering in R Tutorial

# Tag: r tutorial

## Introduction to Bayesian Statistics, Part 3

The two most popular Markov chain Monte Carlo sampling algorithms are Gibbs sampling and Metropolis Hastings. These algorithms produce Markov chains. Numbers inside a Markov chain are dependent on only the previous number. In the context of sampling, we check the probability of the proposed value based on only the probability of the current value,… Continue reading Introduction to Bayesian Statistics, Part 3

## Introduction to Bayesian statistics, Part 2

As mentioned in Part 1, in Bayesian statistics you summarize a priori knowledge in the prior, and your data in the likelihood. The prior distribution is often chosen based on analytical convenience, while the likelihood is chosen based on the underlying sampling distribution (read about some appropriate distributions here). Multiplying these together produces the posterior distribution. Probability… Continue reading Introduction to Bayesian statistics, Part 2

## Analysis of the predictors of Pokemon strength

My presentation on the best predictors of Pokemon strength (as measured by the sum of Pokemon statistics) were analyzed using clustering methods.