- Introduction to Coding to Learn
- Introduction to graphical modeling
- How graphical modeling makes phylogenetic estimation more flexible
- Using RevBayes in the classroom

We’re all familiar with the idea of learning to code

We’re all familiar with the idea of learning to code

- Maybe students in your degree program take an upper-division course where codin is the aim

We’re all familiar with the idea of learning to code

- Maybe students in your degree program take an upper-division course where codin is the aim
- This is the “learn to code” framework. Being able to produce code is the end goal

There are some unsatisfying things about this model

There are some unsatisfying things about this model

- If we confine coding to niche classes, we’re likely to miss much of the population
- Large proportion of students who seek out computational training have seen computation in high school

There are some unsatisfying things about this model

- If we confine coding to niche classes, we’re likely to miss much of the population
- Large proportion of students who seek out computational training have seen computation in high school

== Confining learning to people who are already convinced of the utility

There are some unsatisfying things about this model

- If we confine coding to niche classes, we’re likely to miss much of the population
- Large proportion of students who seek out computational training have seen computation in high school

== Students get coding either late in undergrad, or after they’ve been mucking around unproductively with data

Coding doesn’t have to be the main focus of what you’re doing.

Coding doesn’t have to be the main focus of what you’re doing

**Code to learn:**work with data computationally to develop biological insight

Coding doesn’t have to be the main focus of what you’re doing

**Code to learn:**work with data computationally to develop biological insight

- Example: Rather than talking about Bergmann’s Rule, have students download occurrence data from GBIF and key it to a trait database to observe body size along longitudinal gradients

Citations for many of the things in the previous couple slides can be found in this manuscript

Coding doesn’t have to be the main focus of what you’re doing

- Systematics is a field ripe with opportunities
- To understand systematics, you need to know some

- Biology

- Statistics

- Math

- Geology & Earth history

Coding doesn’t have to be the main focus of what you’re doing

- Systematics is a field ripe with opportunities
- In performing systematics research, we learn about

- Biology

- Statistics

- Math

- Geology & Earth history

RevBayes is a Bayesian modeling software underlain by a graphical model framework

RevBayes is a Bayesian modeling software underlain by a graphical model framework

- RevBayes implements an R-like statistical modeling language called
`Rev`

RevBayes is a Bayesian modeling software underlain by a graphical model framework

- RevBayes implements an R-like statistical modeling language called
`Rev`

- The design of the language is meant to give users control over the details of their analysis

- What is a model?

- What is a model?
- A model uses mathematics to describe a process, or the behavior of a system
- Important facets of the generating process of the data are written down as parameters

- What is a model?
- A model uses mathematics to describe a process, or the behavior of a system
- Important facets of the generating process of the data are written down as parameters

- A graphical model represents the relationships and dependencies between model paramters

- An archer: Let’s say the archer is a pretty good archer

- An archer: Let’s say the archer is a pretty bad archer

- It’s easy to describe a model where we are manipulating one parameter

- But we rarely have such a simple case when we’re analysing biological data

- We’re describing the mechanisms that generated our observed data

- In the case of a tree, this is the process of molecular or morphological evolution

- This is where graphical models come in

- This is where graphical models come in
- A
**graphical model**represents the joint probability distribution as a graph

- This is where graphical models come in
- A graphical model represents the joint probability distribution as a graph
**Nodes**are variables.**Edges**represent dependencies between nodes.

- This is where graphical models come in
- A graphical model represents the joint probability distribution as a graph
**Nodes**are variables.**Edges**represent dependencies between nodes.

- This is where graphical models come in
- A graphical model represents the joint probability distribution as a graph
**Nodes**are variables.**Edges**represent dependencies between nodes.

- This is where graphical models come in
- A graphical model represents the joint probability distribution as a graph
**Nodes**are variables.**Edges**represent dependencies between nodes.

Pr( data | model )

- This is where graphical models come in
- A graphical model represents the joint probability distribution as a graph
**Nodes**are variables.**Edges**represent dependencies between nodes.

- In previous iterations of software, you might have to email a developer and ask them to implement something

- RevBayes’ graphical model framework emphasizes flexibility

Over the next two hours, we’re going to make the case that this framework enables flexible, code-to-learn classrooms