From 31fe55ab73983be900ce1c76f5a1b23974e14b94 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sat, 18 Jul 2026 18:52:15 +1000 Subject: [PATCH 1/3] =?UTF-8?q?=F0=9F=94=84=20resync=20stats=5Fexamples.md?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .translate/state/stats_examples.md.yml | 6 ++++ lectures/stats_examples.md | 50 ++++++++++++++++---------- 2 files changed, 37 insertions(+), 19 deletions(-) create mode 100644 .translate/state/stats_examples.md.yml diff --git a/.translate/state/stats_examples.md.yml b/.translate/state/stats_examples.md.yml new file mode 100644 index 00000000..a9abf32e --- /dev/null +++ b/.translate/state/stats_examples.md.yml @@ -0,0 +1,6 @@ +source-sha: 2944402a4c4a3101e92e2824e10b0dc212265264 +synced-at: "2026-07-18" +model: claude-sonnet-5 +mode: RESYNC +section-count: 8 +tool-version: 0.17.0 diff --git a/lectures/stats_examples.md b/lectures/stats_examples.md index 682a6f37..1a45b61d 100644 --- a/lectures/stats_examples.md +++ b/lectures/stats_examples.md @@ -9,6 +9,17 @@ kernelspec: display_name: Python 3 (ipykernel) language: python name: python3 +translation: + title: 一些概率分布 + headings: + Some Discrete Probability Distributions: 一些离散概率分布 + Geometric distribution: 几何分布 + Pascal (negative binomial) distribution: 帕斯卡（负二项）分布 + Newcomb–Benford distribution: 纽科姆-本福特分布 + Univariate Gaussian distribution: 一元高斯分布 + Uniform Distribution: 均匀分布 + A Mixed Discrete-Continuous Distribution: 混合离散-连续分布 + Drawing a Random Number from a Particular Distribution: 从特定分布中抽取随机数 --- # 一些概率分布 @@ -70,11 +81,13 @@ $$ 让我们使用Python从该分布中抽取观测值，并将样本均值和方差与理论结果进行比较。 ```{code-cell} ipython3 +rng = np.random.default_rng() + # 指定参数 p, n = 0.3, 1_000_000 # 从分布中抽取观测值 -x = np.random.geometric(p, n) +x = rng.geometric(p, n) # 计算样本均值和方差 μ_hat = np.mean(x) @@ -116,19 +129,19 @@ $$ $$ ```{code-cell} ipython3 -# specify parameters +# 指定参数 r, p, n = 10, 0.3, 1_000_000 -# draw observations from the distribution -x = np.random.negative_binomial(r, p, n) +# 从分布中抽取观测值 +x = rng.negative_binomial(r, p, n) -# compute sample mean and variance +# 计算样本均值和方差 μ_hat = np.mean(x) σ2_hat = np.var(x) -print("The sample mean is: ", μ_hat, "\nThe sample variance is: ", σ2_hat) -print("\nThe population mean is: ", r*(1-p)/p) -print("The population variance is: ", r*(1-p)/p**2) +print("样本均值为：", μ_hat, "\n样本方差为：", σ2_hat) +print("\n总体均值为：", r*(1-p)/p) +print("总体方差为：", r*(1-p)/p**2) ``` ## 纽科姆-本福特分布 @@ -209,7 +222,7 @@ $$f(x|u,\sigma^2)=\frac{1}{\sqrt{2\pi \sigma^2}}e^{[-\frac{1}{2\sigma^2}(x-u)^2] n = 1_000_000 # 从分布中抽取观测值 -x = np.random.normal(μ, σ, n) +x = rng.normal(μ, σ, n) # 计算样本均值和方差 μ_hat = np.mean(x) @@ -251,7 +264,7 @@ a, b = 10, 20 n = 1_000_000 # 从分布中抽取观测值 -x = a + (b-a)*np.random.rand(n) +x = a + (b-a)*rng.random(n) # 计算样本均值和方差 μ_hat = np.mean(x) @@ -287,9 +300,9 @@ $$ 让我们先生成一个随机样本并计算样本矩。 ```{code-cell} ipython3 -x = np.random.rand(1_000_000) +x = rng.random(1_000_000) # x[x > 0.95] = 100*x[x > 0.95]+300 -x[x > 0.95] = 100*np.random.rand(len(x[x > 0.95]))+300 +x[x > 0.95] = 100*rng.random(len(x[x > 0.95]))+300 x[x <= 0.95] = 0 μ_hat = np.mean(x) @@ -429,13 +442,13 @@ $$ n, λ = 1_000_000, 0.3 # 生成均匀分布随机数 -u = np.random.rand(n) +u = rng.random(n) # 转换 x = -np.log(1-u)/λ # 生成指数分布 -x_g = np.random.exponential(1 / λ, n) +x_g = rng.exponential(1 / λ, n) # 绘图并比较 plt.hist(x, bins=100, density=True) @@ -505,13 +518,13 @@ $$ n, λ = 1_000_000, 0.8 # 生成均匀分布随机数 -u = np.random.rand(n) +u = rng.random(n) # 转换 x = np.ceil(np.log(1-u)/np.log(λ) - 1) # 生成几何分布 -x_g = np.random.geometric(1-λ, n) +x_g = rng.geometric(1-λ, n) # 绘图并比较 plt.hist(x, bins=150, density=True) @@ -520,7 +533,7 @@ plt.show() ```{code-cell} ipython3 -np.random.geometric(1-λ, n).max() +rng.geometric(1-λ, n).max() ``` ```{code-cell} ipython3 @@ -530,5 +543,4 @@ np.log(0.4)/np.log(0.3) ```{code-cell} ipython3 plt.hist(x_g, bins=150, density=True, alpha=0.6) plt.show() -``` - +``` \ No newline at end of file From 0d00b9af106d09b6fb531116166989cb99159e08 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sat, 18 Jul 2026 21:21:03 +1000 Subject: [PATCH 2/3] Restore CJK font configuration dropped by the resync The resynced file keeps Chinese plot labels but reverted the import cell to the source's exact form, losing the Source Han Serif setup - Chinese in figures would render as missing glyphs. Residual #107 class found in the merge review; applied wave-wide. Co-Authored-By: Claude Fable 5 --- lectures/stats_examples.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/lectures/stats_examples.md b/lectures/stats_examples.md index 1a45b61d..15f566b5 100644 --- a/lectures/stats_examples.md +++ b/lectures/stats_examples.md @@ -44,6 +44,10 @@ tags: [hide-output] ```{code-cell} ipython3 import numpy as np import matplotlib.pyplot as plt +import matplotlib as mpl +FONTPATH = "fonts/SourceHanSerifSC-SemiBold.otf" +mpl.font_manager.fontManager.addfont(FONTPATH) +plt.rcParams['font.family'] = ['Source Han Serif SC'] import prettytable as pt from mpl_toolkits.mplot3d import Axes3D from matplotlib_inline.backend_inline import set_matplotlib_formats From 2fa915b5b079530a4d2342c65e87ef4b24e763b1 Mon Sep 17 00:00:00 2001 From: Matt McKay Date: Sun, 19 Jul 2026 14:04:43 +1000 Subject: [PATCH 3/3] Restore trailing newline (engine issue tracked in QuantEcon/action-translation#116) Co-Authored-By: Claude Fable 5 --- lectures/stats_examples.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/lectures/stats_examples.md b/lectures/stats_examples.md index 15f566b5..c27f3112 100644 --- a/lectures/stats_examples.md +++ b/lectures/stats_examples.md @@ -547,4 +547,4 @@ np.log(0.4)/np.log(0.3) ```{code-cell} ipython3 plt.hist(x_g, bins=150, density=True, alpha=0.6) plt.show() -``` \ No newline at end of file +```