Hey, I'm Matthew 👋
RSS FeedWelcome to my personal blog. I write about statistics, data, and whatever I'm building. This is where I think out loud, keep notes worth sharing, and rant about bad stats I see in the wild.
Browse the posts or read a bit about me . You can also visit my portfolio .
Featured
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mmm-framework 0.1.0 Is on PyPI
The first public release of mmm-framework — a Bayesian marketing-mix modeling library built on PyMC-Marketing, designed around methodological rigor instead of specification shopping. pip install mmm-framework.
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Hello, world
Welcome to my corner of the internet. A quick note on what this blog is for.
Recent Posts
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Collinearity Doesn't Break Your Model — It Tells You What Your Data Can't Separate
A high VIF isn't a technical failure to fix — it's a signal that your data can't distinguish two effects. The right response is better data or informative priors, not variable deletion.
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Simulation-Based Calibration: The Missing Test in Your Bayesian Workflow
Passing R-hat and ESS tells you the sampler converged — it says nothing about whether the inference is correct. Simulation-Based Calibration is the test that checks the inference itself.
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Read the Diagnostics First
R-hat, ESS, and divergences are not bureaucratic hurdles before you report a ROAS — they're the sampler telling you whether its output is valid. Here's what each one actually measures and what it reveals when it fails.
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Stop Reporting ROI to Four Significant Figures
A point estimate written as 2.347 looks like you know the answer to a tenth of a percent. If your interval is ±10%, you don't — you know the first digit and you're guessing at the second. The decimal places are a confidence claim the model never made.