[action-translation] resync: prob_meaning.md#147
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Pull request overview
This PR forward-resyncs the Chinese translation of prob_meaning.md to match the current upstream lecture, replacing older class-based / prettytable-based code and missing sections with the upstream’s function-based narrative and updated Bayesian content.
Changes:
- Resynced
lectures/prob_meaning.mdto the upstream structure, including updated frequentist simulation code and expanded Bayesian posterior/concentration discussion. - Added translation front-matter (title + per-heading mapping) for consistent section alignment.
- Added translation state tracking file recording source SHA / sync metadata.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 3 comments.
| File | Description |
|---|---|
| lectures/prob_meaning.md | Whole-lecture resync to upstream structure/content, including updated code cells, exercises/solutions, and new/updated sections. |
| .translate/state/prob_meaning.md.yml | Records the source commit SHA and resync metadata for the translation pipeline. |
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| import numpy as np | ||
| import pandas as pd | ||
| import prettytable as pt | ||
| import matplotlib.pyplot as plt | ||
| from scipy.stats import binom | ||
| import scipy.stats as st |
| def simulate_head_counts(θ, n, I, seed=1234): | ||
| "模拟 I 个长度为 n 的投掷序列；返回每个序列中出现正面的次数。" | ||
| rng = np.random.default_rng(seed) | ||
| Y = (rng.random((I, n)) <= θ).astype(int) | ||
| return Y.sum(axis=1) |
| @@ -102,11 +106,10 @@ $$ | |||
| 令 $f_k$ 记录长度为 $n$ 的样本中满足 $\sum_{h=1}^n y_h^i = k$ 的比例: | |||
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 <noreply@anthropic.com>
✅ Translation Quality ReviewVerdict: PASS | Model: claude-sonnet-5 | Date: 2026-07-19 📝 Translation Quality
Summary: This is a high-quality, comprehensive translation that accurately conveys the technical and conceptual content of the English source, including all mathematical formulas, code blocks, and nuanced explanations of frequentist vs. Bayesian interpretations. Minor improvements could be made in consistently using the glossary term '贝塔分布' instead of the English loanword 'beta' in phrases like 'beta先验' and 'beta密度', and a few phrases could be slightly smoothed for better academic fluency. No syntax errors were found, and formatting is fully preserved. Mathematical notation and LaTeX equations are fully preserved and correctly rendered throughout all changed sections Code cells and their docstrings/comments are accurately and naturally translated into Chinese Technical terms like '大数定律', '后验分布', '先验分布', '二项分布' are used consistently and match the glossary The overall structure, headings, and exercise/solution blocks are faithfully preserved with correct MyST directive syntax Complex explanatory passages (e.g., on posterior concentration and Bayesian coverage intervals) are translated with high fidelity to the original meaning while reading naturally in Chinese Suggestions:
🔍 Diff Quality
Summary: The resync correctly restructures the target to match the current source, updating headings, content, and code examples while properly maintaining the translation metadata map. This review was generated automatically by action-translation review mode. |
…anslation#116) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Forward Resync: prob_meaning.md
Source: QuantEcon/lecture-python.myst — lectures/prob_meaning.md
Source commit:
1f15346This PR resyncs the translation to match the current source document.
Reason: The target uses an entirely different, older code structure (a 'frequentist' and 'Bayesian' class-based approach with prettytable) rather than the source's function-based approach, including extra code and explanations not in the source (e.g., detailed class methods, additional plots like scatter plots of quantiles, extended derivations). It also has extra font-loading setup and different exercise/solution text (e.g., referencing different external videos/links vs source's YouTube links). Conversely, the target is missing significant source content such as the newer 'Comparing the two interpretations' section, the closed-form derivation exercise (pm_ex2 in source differs completely from target's version), the 'Why the posterior concentrates' section with variance derivation, and box-and-whisker plot code. Since target has unique additions (class-based code, extra plots, different explanations) not present in source, and source has content missing from target, the verdict is TARGET_HAS_ADDITIONS per the priority rule.
Changes
Whole-file resync applied. The entire document was resynced in a single pass.
Created by action-translation forward resync