uncertain-rs

A Rust library for uncertainty-aware programming, implementing the approach from "Uncertain: A First-Order Type for Uncertain Data" by Bornholt, Mytkowicz, and McKinley.

Core Concept: Evidence-Based Conditionals

Instead of treating uncertain data as exact values (which leads to bugs), this library uses evidence-based conditionals that account for uncertainty:

use uncertain_rs::Uncertain;

// Create uncertain values from probability distributions
let speed = Uncertain::normal(55.2, 5.0); // GPS reading with ±5 mph error

// Evidence-based conditional (returns Uncertain<bool>)
let speeding_evidence = speed.gt(60.0);

// Convert evidence to decision with confidence level
if speeding_evidence.probability_exceeds(0.95) {
    // Only act if 95% confident
    println!("Issue speeding ticket");
}

Features

• Evidence-based conditionals: Comparisons return evidence, not boolean facts
• Uncertainty propagation: Arithmetic operations preserve uncertainty
• Lazy evaluation: Computation graphs built lazily for efficiency
• SPRT hypothesis testing: Sequential Probability Ratio Test for optimal sampling
• Rich distributions: Normal, uniform, exponential, binomial, categorical, etc.
• Statistical analysis: Mean, std dev, confidence intervals, CDF, etc.

Installation

Add this to your Cargo.toml:

[dependencies]
uncertain-rs = "0.1.0"

Quick Start

use uncertain_rs::Uncertain;

fn main() {
    // Create uncertain values
    let x = Uncertain::normal(5.0, 1.0);
    let y = Uncertain::normal(3.0, 0.5);

    // Perform arithmetic operations
    let sum = x + y;
    let product = x * y;

    // Sample from the distributions
    println!("Sum sample: {}", sum.sample());
    println!("Product sample: {}", product.sample());

    // Statistical analysis
    println!("Sum mean: {}", sum.expected_value(1000));
    println!("Sum std dev: {}", sum.standard_deviation(1000));
}

For more examples, see the examples directory.

License

This project is licensed under the MIT License - see the LICENSE file for details.

Contributing

Contributions are welcome! Please feel free to submit a Pull Request.