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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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.
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The Assumptions Are the Model
A model is a dumb number-crunching machine. Put data in, get numbers out — and unless something breaks loudly, those numbers look exactly as confident when your assumptions hold as when you've shredded them. The meaning was never in the arithmetic. It was in the assumptions.