I'm primarily a self-taught programmer. I have taken a handful of classes, which is precisely why I'm pro teach yourself. Having a vision for what you want to create with code makes everything so much easier. Pseudo code So, you have some idea about what you want to program. Start by writing pseudo-code. This is… Continue reading How to teach yourself coding
This code introduces how to perform parameter estimation for a system of differential equations in R. First, the necessary packages and data are imported using code previously introduced in a previous post. R code available here. Due to computational constraints, the mathematical model we are using to fit to the data includes product inhibition. If product inhibition doesn't… Continue reading Introduction to modeling: parameter estimation in R
After introducing students to a simple mathematical model describing enzyme kinetics, I introduced them to coding and modeling in R. Coding is traditionally done by first describing some coding 'rules', including ending the line with semicolons or how to define a variable. Personally I found these methods ineffective and frustrating. I took a few classes… Continue reading Introduction to modeling and coding in R
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
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
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
Games of chance are often people’s first exposure to statistics. Settlers of Catan is a game that revolves around the probability distribution of two independent 6-sided die rolls. The game consists of hexagons with one of four possible resources available. These hexagons are normally in a random configuration. Each hexagon receives a random number token.… Continue reading Random Chance in Settlers of Catan
Example code can be found on my github.
Here is powerpoint for teaching programming for mathematical biology. Referenced files are available on my github.