Building bayesim: The journey begins

Written:
  • #tutorial
  • #R
  • #simulations

uilding a framework for simulation studies in Bayesian statistics has been a journey of discovery. Let me share some thoughts on what I’m working on with bayesim.

The Motivation

Simulation studies are essential for understanding statistical methods, but setting them up properly is often more complex than it first appears. You need to:

  • Generate data from known processes
  • Fit multiple models with different specifications
  • Calculate meaningful performance metrics
  • Handle computational challenges (convergence, runtime)
  • Organize results for analysis

After doing this manually several times, I realized there was value in creating a framework that handles the common infrastructure while remaining flexible for different research questions.

Design Philosophy

The core idea behind bayesim is to separate concerns:

  1. Data generation: Users provide functions that generate data according to their research needs
  2. Model fitting: The framework handles the mechanics of fitting multiple model specifications
  3. Metric calculation: Standardized metrics are computed automatically
  4. Result organization: Everything is organized into tidy data structures

This separation allows researchers to focus on their specific research questions rather than the plumbing.

Current Status

The package is still in early development, but the basic architecture is taking shape. The main components include:

  • A flexible data generation interface
  • Integration with popular Bayesian packages (brms, rstanarm)
  • A growing collection of performance metrics
  • Tools for organizing and analyzing results

Example Usage

Here’s a simple example of what the interface might look like:

library(bayesim)

# Define a data generating function
my_dgp <- function(n, effect_size, ...) {
  x <- rnorm(n)
  y <- effect_size * x + rnorm(n)
  data.frame(x = x, y = y)
}

# Define model specifications
models <- list(
  simple = bf(y ~ x),
  complex = bf(y ~ poly(x, 2))
)

# Run the simulation
results <- run_simulation(
  dgp = my_dgp,
  models = models,
  n_sims = 100,
  dgp_args = list(n = 200, effect_size = 0.5)
)

# Analyze results
summarize_results(results)

Challenges and Next Steps

Building a general framework while maintaining flexibility is challenging. Some areas I’m still working through:

  • Interface design: Balancing simplicity with power
  • Performance: Making large simulations computationally feasible
  • Error handling: Gracefully dealing with convergence issues
  • Documentation: Making it easy for others to get started

Get Involved

If you’re interested in Bayesian simulation studies or have ideas for the framework, I’d love to hear from you. The project is open source and welcomes contributions.

You can find the current development version on GitHub. Fair warning: it’s still very much a work in progress!

Looking Forward

This is just the beginning of what I hope will become a useful tool for the Bayesian community. There’s still a lot of work to do, but I’m excited about the potential.

Stay tuned for more updates as the project develops!