Evaluation | Learning | Human sovereignty
- Searching for rebirth and reinvention
- Independent evaluation
- Finding parameters with meaning
- Applied generative AI for program monitoring & evaluation
- What can be wrought from data + assumptions
- Empirical methods in qualitative inquiry
- The computational grasp of natural language
- Failed and fragile states
- Human frontiers
We've all had moments of grace in our professional lives1 - opportunities to learn, grow, and perform at exponential leaps from our existing skills or experience. I hope I've made the most of mine.. I do know that I have squandered at least one. The following is a selective list of my work history that is most meaningful to me. Note that meaningful does not necessarily mean successful, whatever that may mean and to whom.
We all need to keep ourselves current on skills. Here are my notes on various courses or textbooks.
An intermediate text on Bayesian analysis
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- Deriving Bayes Rule and its use in a data analysis
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- Bayesian inference for the binomial case
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- Conducting inference with samples from the posterior
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- Bayesian inference for the normal case
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- Metropolis algorithm
Advanced text on Bayesian analysis
A dense text focusing more on the mathematics used to do data science
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- Markov's Inequality, Chebushev's Inequality, Law of Large Numbers, and other bounds
Footnotes
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If you've been out in the world a few years and have not yet experienced one of these, you are due. But also check yourself - it will never happen if you don't put yourself out there ↩