Bob Carpenter, Daniel Lee, and Michael Betancourt will be teaching the 3-day class starting on 19 September in Paris. Following is the outline for the course:
Day 1
Introduction to Bayesian statistics
- Likelihood / sampling distributions
- Priors, Posteriors via Bayes’s rule
- Posterior expectations and quantiles
- Events as expectations of indicator functions
Introduction to Stan
- Basic data types
- Variable declarations
- Constrained parameters and transforms to unconstrained
- Program blocks and execution
- Derived quantities
- Built-in functions and operators
- Statements: sampling, assignment, loops, conditionals, blocks
- How to use Stan within R with RStan
Hands-on examples
Day 2
ODE and PK/PD Modeling
- Parameters and data to ODEs
- Non-stiff ODE solver
- Stiff ODE solver
- Control parameters and tolerances
- Coupled ODE systems for sensitivities
- Elimination half-lifes
Inference with Markov chain Monte Carlo
- Monte Carlo methods and plug-in inference
- Markov chain Monte Carlo
- Convergence diagnostics, R-hat, effective sample size
- Effective sample size vs. number of iterations
- Plug-in posterior expectations and quantiles
- Event probability calculations
Hands-on examples
Day 3
Additional Topics in PK/PD Modeliong
- Bolus and infusion dosing
- Lag time and absorption models
- Linear versus Michaelis/Menten elimination
- Hierarchical models for patient-level effects
- Transit compartment models and time lags
- Multi-compartment models and varying time scales
- Joint PK/PD modeling: Bayes vs. “cut”
- Meta-analysis
- Formulating informative priors
- Clinical trial simulations and power calculations
Stan programming techniques
- Reproducible research practices
- Probabilistic programming principles
- Generated quantities for inference
- Data simulation and model checking
- Posterior predictive checks
- Cross-validation and predictive calibration
- Variable transforms for sampling efficiency
- Multiple indexing and range slicing
- Marginalizing discrete parameters
- Handling missing data
- Ragged and aparse data structures
- Identifiability and problematic posteriors
- Weakly informative priors
If you are in Europe in September, please come and join us. Thanks to Julie Bertrand and France Mentré from Université Paris Diderot for helping us organize the course.
You can register here.
Will slides be posted for this course, or will that be proprietary?
It seems they always post slides on mc-stan.org.
We’re very committed to being open.
Our slides will go up online. Probably after the course, because we’re always tuning them until the last minute.
Feedback is always appreciated, too.
We’re also working on an intro to applied Bayesian modeling with Stan and we already have an agreement in place with Chapman & Hall/CRC to let us distribute PDFs for free. Same for the econometrics book we’re writing with Jim Savage (also Chapman & Hall/CRC).
Gratuitous tip: Post the draft book-chapter pdf’s online. You can sometimes get great feedback for improving the book.
We should be able to make the slides available.
I’m signed up for this. I don’t know anything about pharmacometrics, but I look forward to learning about it. You folks should have more such courses in western Europe. If I had money I’d invite you over to Potsdam.
I think most of us love to travel, so yes this would be great although it makes it harder for us to organize as we do not have a permanent presence in Europe. If anyone local wants to collaborate with us, let me know.
Are you planning to offer it on Internet ?
At some point, we want to try an online class as there seems to be some demand for it, but no immediate plans. I will post it here if/when we decide to do it.