Gay Monte Carlo Measurements
name: gay-monte-carlo description: Monte Carlo uncertainty propagation with Gay.jl deterministic coloring and Enzyme.jl autodiff for gamut-aware probability distributions. trit: 1 color: "#77DEB1"
Overview
GayMonteCarloMeasurements.jl extends MonteCarloMeasurements.jl with Gay.jl chromatic identity for deterministic color-coded uncertainty propagation.
Core Concepts
Particles as Colored Distributions
using MonteCarloMeasurements
using Gay
# Construct uncertain parameters with color tracking
gay_seed!(0xcd0a0fde6e0a8820)
a = π ± 0.1 # Particles{Float64,2000}
# Propagate through nonlinear functions
sin(a) # → Particles with full distribution
Enzyme Gamut Learning
using Enzyme
# Learnable colorspace parameters
params = OkhslParameters()
function loss(params, seed, target_gamut=:srgb_boundary)
color = forward_color(params, projection, seed)
gamut_penalty = out_of_gamut_distance(color, target_gamut)
bandwidth_reward = color_distinctiveness(color)
return gamut_penalty - 0.1 * bandwidth_reward
end
∂params = Enzyme.gradient(Reverse, loss, params, seed)
Features
- Nonlinear uncertainty propagation - Handles x², sign(x), integration
- Correlated quantities - Multivariate particles
- Distribution fitting -
fit(Gamma, p)for any Particles - Visualization -
plot(p)shows histogram,density(p)shows KDE - SPI verification - Fingerprint matching across network
GF(3) Integration
| Trit | Role | Operation | |------|------|-----------| | +1 | PLUS | Generative sampling | | 0 | ERGODIC | Distribution transport | | -1 | MINUS | Constraint verification |
Self-Avoiding Walk
next_color() → visited check
│
├─ fresh → XOR into fingerprint
│
└─ collision → triadic fork
Repository
- Source: bmorphism/GayMonteCarloMeasurements.jl
- Seed:
0xcd0a0fde6e0a8820 - Index: 103/1055
Related Skills
gay-julia- Core Gay.jl integrationspi-parallel-verify- Fingerprint verificationfokker-planck-analyzer- Equilibrium analysis