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KDE is a non-parametric way to estimate the probability density function of a random variable.\n\n## Description\n\nThe project generates a mixture of two normal distributions and uses different kernel functions (Epanechnikov, Gaussian, Tricube, and Quartic) to estimate the underlying density function.\nIt then compares the estimated density with the true density function and generates a histogram of the samples.\n\n\n## Dependencies\n\nThis project uses the following dependencies:\n\n- `peroxide`: For random number generation, cubic hermite spline, piecewise rejection sampling and plotting.\n- `rayon`: For parallel computation.\n\n## Usage\n\nTo run the project, simply compile and execute the `main.rs` file:\n\n```\ncargo run --release\n```\n\nThis will generate the `kde.png` and `histogram.png` files in the project directory.\n\n## Results\n\n1. `kde.png`: A plot showing the true density function and the estimated density function using KDE.\n   ![kde.png](kde.png)\n2. `histogram.png`: A plot showing the true density function and a histogram generated using the samples obtained from the estimated density function.\n   ![histogram.png](histogram.png)\n\n## Contributing\n\nContributions are welcome! 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