Second Brain
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A structured knowledge base covering Bayesian statistics, econometrics, causal inference, and agent-based modeling. Notes are cross-linked by topic — use the graph view or search bar to explore connections across the collection.
Selected Analyses
carryover dynamics interact with the timing of sequential media experiments (delayed outcomes)?
July 1, 2026 · Market Response · Bayesian Experimental Design · Bayesian Statistics
Carryover means an intervention’s effect is spread over future periods, so a sequential experimentation loop faces delayed outcomes: you cannot read a test’s result — or start a clean next test — until the adstock has decayed.
When should continuous media learning use Bayesian experimental design vs Bayesian optimization vs a bandit?
July 1, 2026 · Market Response · Bayesian Experimental Design · Probabilistic Numerics · Bayesian Statistics
All three sit on the same Bayesian surrogate of the response surface and differ only in objective.
How would a geo-holdout experiment be encoded as a design ξ and its EIG computed against an MMM posterior?
July 1, 2026 · Market Response · Bayesian Experimental Design · Bayesian Statistics
Encode the geo-holdout as a design vector = which geos get their spend perturbed, on which channel(s), by how much, and over which weeks.
What would continuous learning look like in media measurement, given that media has interaction effects and learning all interactions is costly or needs more cells than available techniques support?
July 1, 2026 · Market Response · Bayesian Experimental Design · Bayesian Statistics · Probabilistic Numerics
Continuous learning in media measurement is a closed loop: a Bayesian surrogate of the response surface (a media-mix model or GP) is continually re-fit as data arrive; an active-experimentation layer then picks the next spend allocation / geo-test to run by maximizing expected information gain about the effects — including interactions — that are still uncertain and decision-relevant.
How can SMM be used to calibrate agent based models?
April 11, 2026 · Agent Based Modeling · Calibration · Simulation Estimation · Econometrics
The Simulated Method of Moments (SMM) calibrates an ABM by choosing structural parameters to minimize a weighted distance between observed macro-level data moments and their simulated counterparts produced by running the ABM at .
Knowledge Base
| Domain | Core Topics |
|---|---|
| Bayesian Statistics | Inference fundamentals, hierarchical models, MCMC, model checking |
| Econometrics | Identification strategies, regression foundations, simulation-based estimation |
| Agent-Based Modeling | Calibration methods, social dynamics, consumer behavior |
| Research Methodology | Experimental design, multiple comparisons, causal reasoning |
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