computational-inference
Computational methods for statistical inference and optimization
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill computational-inference --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Computational Inference **Advanced computational methods for statistical inference in complex models** Use this skill when working on: MCMC algorithms, importance sampling, Bayesian inference, parallel computing for statistics, GPU acceleration, or computationally intensive inference procedures. --- ## Monte Carlo Methods ### Fundamental Principle Monte Carlo methods approximate expectations via random sampling: $$E[g(X)] \approx \frac{1}{N} \sum_{i=1}^{N} g(X_i), \quad X_i \sim P$$ **Monte Carlo Standard Error**: $$\text{MCSE} = \frac{\hat{\sigma}}{\sqrt{N}}$$ ### Variance Reduction Techniques | Technique | Idea | Variance Reduction | |-----------|------|-------------------| | Antithetic variates | Use negatively correlated pairs | Up to 50% | | Control variates | Subtract known expectation | Depends on correlation | | Importance sampling | Sample from better distribution | Can be dramatic | | Stratified sampling | Sample from strata separately | Reduces variance | ### R Implementation ```r #' Monte Carlo Integration with Variance Reduction #' #' @param g Function to integrate #' @param sampler Function that generates samples #' @param n Number of samples #' @param method Varian
- Monte Carlo Methods
- Fundamental Principle
- Variance Reduction Techniques
- R Implementation
- Importance Sampling
- Theory
- Self-Normalized Importance Sampling
- Effective Sample Size
- MCMC Methods
- Metropolis-Hastings Algorithm
- Gibbs Sampling
- MCMC Diagnostics
- Hamiltonian Monte Carlo
- Key Parameters
What does the computational-inference skill do?
Computational methods for statistical inference and optimization
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill computational-inference --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.