🐍 central_limit_theorem.py
This project demonstrates how the Central Limit Theorem can be implemented to estimate the mean of a distribution. As an example, I take the chi-squared distribution and use it to estimate its mean by:
Drawing 1000 samples from the chi-squared distribution, where each sample is an arithmetic mean of N random observations. N is set by the user. Fitting a Gaussian distribution to each of the 1000 sample distributions. Estimating the mean of the chi-squared distribution from the Gaussian distribution. Comparing the estimated mean with the true mean of the chi-squared distribution.
🐍 find_global_min_of_func.py
This project uses the differential_evolution method to find the global minimum of the function:
np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)
The user specifies the range of x-values in which to search for the minimum.
🐍 find_min_of_func.py
This project uses the BFGS method to find the minimum of the function:
np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)
The user specifies the initial approximation of the x-coordinate of the minimum.
🐍 linalg_solve_function.py
This project fits an n-degree polynomial to the function:
np.sin(x / 5) * np.exp(x / 10) + 5 * np.exp(-x / 2)
The user specifies several x-coordinates for the polynomial to pass through.