idrk - A Programming Language for Exploring Uncertainty

Introduction

idrk is a dynamic programming language designed to explore uncertainty in data. It is particularly useful in statistical simulations, where the result of an experiment is not deterministic. idrk is capable of handling distributions, random variables, and probabilistic programming constructs.

Syntax

idrk has a syntax similar to Python. It supports many of the same data structures and functions. Here is an example of a for loop in idrk:

for i in range(10):
    print(i)

idrk also has special syntax to work with distributions. Here's an example of generating a normal distribution:

import idrk.distributions as dist

mu = 0
sigma = 1

X = dist.Normal(mu, sigma)

X is now a random variable, which can be used in other calculations.

Probabilistic Programming

idrk supports probabilistic programming, which allows for more complex models. Here is an example of Bayesian regression:

import idrk.ppl as ppl

def model(x):
    slope = ppl.Normal(0, 1)
    intercept = ppl.Normal(0, 1)
    noise = ppl.Exponential(1)

    y = slope * x + intercept + noise

    return y

data = [(1, 2), (2, 4), (3, 6)]

posterior = ppl.infer(model, data)

The model function defines a probabilistic model for the data. ppl.infer uses Markov Chain Monte Carlo (MCMC) to estimate the posterior distribution of the model parameters.

Conclusion

idrk is a powerful tool for exploring uncertainty in data. With its support for distributions and probabilistic programming, it is capable of handling complex models.