File:Convergence of multinomial distribution to the gaussian distribution.webm
Convergence_of_multinomial_distribution_to_the_gaussian_distribution.webm (file size: 3.68 MB, MIME type: video/webm)
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DescriptionConvergence of multinomial distribution to the gaussian distribution.webm |
English: See
https://en.wikipedia.org/wiki/Multinomial_distribution#Large_deviation_theory for details of what this image shows. ```python import numpy as np import matplotlib.pyplot as plt from scipy.stats import multinomial from matplotlib.patches import RegularPolygon import os from tqdm import trange M, N = 100000, 10 for N in trange(2, 200): p = np.array([0.2, 0.3, 0.5]) samples = multinomial.rvs(N, p, size=M).T K = np.array([[-np.sqrt(1/2), np.sqrt(1/2), 0], [-np.sqrt(1/6), -np.sqrt(1/6), np.sqrt(4/6)]]) result = np.dot(K, samples) / N triangle_vertices = np.array([K[:, 0], K[:, 1], K[:, 2]]) def f(x, y): return -N/2 * np.sum((np.array([1/3, 1/3, 1/3]) + x * K[0,:] + y*K[1,:] - p)**2 / p, axis=0) x_values = np.linspace(-np.sqrt(1/2), np.sqrt(1/2), 50) y_values = np.linspace(-np.sqrt(1/6), np.sqrt(4/6), 50) X, Y = np.meshgrid(x_values, y_values) Z = np.zeros_like(X) for i in range(X.shape[0]): for j in range(X.shape[1]): Z[i, j] = f(X[i, j], Y[i, j]) hexbin_x = result[0] hexbin_y = result[1] plt.figure(figsize=(10, 10 * np.sqrt(3))) plt.hexbin(hexbin_x, hexbin_y, gridsize=50, cmap='YlGnBu', extent=(min(result[0]), max(result[0]), min(result[1]), max(result[1])), bins='log', mincnt=1, alpha=0.7, edgecolors='gray', linewidths=0.1) # Overlay heatmap of function f within the equilateral triangle plt.imshow(Z, extent=(-np.sqrt(1/2), np.sqrt(1/2), -np.sqrt(1/6), np.sqrt(4/6)), origin='lower', cmap='coolwarm', alpha=0.5) # Plot equilateral triangle triangle = plt.Polygon(triangle_vertices, edgecolor='black', closed=True, fill=False) plt.gca().add_patch(triangle) plt.xlim(-np.sqrt(1/2), np.sqrt(1/2)) plt.ylim(-np.sqrt(1/6), np.sqrt(4/6)) plt.title(f"N={N}, p={p}") plt.gca().set_aspect('equal', adjustable='box') plt.axis('off') dir_path = f"./multinomial" if not os.path.exists(dir_path): os.makedirs(dir_path) plt.savefig(f"{dir_path}/{N:03d}.png",bbox_inches='tight') plt.close() import imageio.v3 as iio import os from natsort import natsorted import moviepy.editor as mp for dir_path in ["./multinomial"]: file_names = natsorted((fn for fn in os.listdir(dir_path) if fn.endswith('.png'))) # Create a list of image files and set the frame rate images = [] fps = 12 # Iterate over the file names and append the images to the list for file_name in file_names: file_path = os.path.join(dir_path, file_name) images.append(iio.imread(file_path)) filename = dir_path[2:] clip = mp.ImageSequenceClip(images, fps=fps) clip.write_videofile(f"{filename}.mp4") !ffmpeg -i multinomial.mp4 -c:v libvpx-vp9 -b:v 0 -crf 10 -c:a libvorbis multinomial.webm ``` |
Date | |
Source | Own work |
Author | Cosmia Nebula |
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14 September 2023
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3,860,200 byte
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Date/Time | Dimensions | User | Comment | |
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current | 22:25, 14 September 2023 | (3.68 MB) | imagescommonswiki>Cosmia Nebula | Uploaded while editing "Multinomial distribution" on en.wikipedia.org |
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