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adaptive-rejection-sampler

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Sample from log-concave distributions

Data & Analytics#r programming#adaptive rejection sampling#ars#log-concave distributions#monte carlo methods#statistical sampling
AuthorZurybr
Version1.0.0
Installs0

System Documentation

What problem does it solve?

This Skill provides guidance for implementing adaptive rejection sampling (ARS) algorithms, which are essential for efficiently generating random samples from complex log-concave probability distributions, particularly in statistical computing.

Core Features & Use Cases

  • ARS Algorithm Implementation: Detailed steps and pseudocode for constructing adaptive upper and lower bounds (envelopes).
  • Log-Concavity Verification: Guidance on correctly checking the log-concavity requirement of target distributions.
  • Domain and Boundary Handling: Strategies for managing unbounded, lower-bounded, upper-bounded, and fully bounded domains.
  • Use Case: When performing Bayesian inference or Monte Carlo simulations that require sampling from custom, log-concave probability densities in R, this skill helps ensure a robust and correct implementation.

Quick Start

Implement the adaptive rejection sampling algorithm in R, ensuring proper handling of log-concavity and domain boundaries.

Dependency Matrix

Required Modules

None required

Components

references

💻 Claude Code Installation

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Please help me install this Skill:
Name: adaptive-rejection-sampler
Download link: https://github.com/Zurybr/lefarma-skills/archive/main.zip#adaptive-rejection-sampler

Please download this .zip file, extract it, and install it in the .claude/skills/ directory.
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