[action-translation] resync: mccall_fitted_vfi.md#132
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Pull request overview
This PR forward-resyncs the Chinese lecture mccall_fitted_vfi.md to match the current upstream (English) source, replacing the prior (older / different-model) content with the newer JAX-based continuous Markov wage process + fitted value function iteration exposition and added simulation/exercise sections.
Changes:
- Replaced the lecture’s model/solution/implementation with the upstream JAX + CRRA + continuous Markov wage process version, including Monte Carlo integration and simulation sections.
- Added translation front-matter metadata (
translation:) and updated section structure/headings accordingly. - Added translation sync state tracking file under
.translate/state/.
Reviewed changes
Copilot reviewed 2 out of 2 changed files in this pull request and generated 6 comments.
| File | Description |
|---|---|
| lectures/mccall_fitted_vfi.md | Whole-lecture resync to upstream content (JAX implementation, revised model, simulations, updated exercises). |
| .translate/state/mccall_fitted_vfi.md.yml | Records resync metadata (source SHA, date, tool/model info). |
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| ```{include} _admonition/gpu.md | ||
| ``` |
| 本讲座延续了{doc}`前一讲 <mccall_model_with_separation>`中介绍的带有离职情形的工作搜寻模型。 | ||
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| 那一讲将外生的工作离职事件与马尔可夫工资报价过程结合了起来。 | ||
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| ## 模型 | ||
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| 该模型与我们{doc}`之前学习的 <mccall_model_with_separation>`带有离职情形的McCall模型相同，除了工资分布是连续的。 | ||
| 假设读者已经熟悉{doc}`mccall_model_with_sep_markov`的内容，该模型可以概括如下。 |
| 与{doc}`mccall_model_with_sep_markov`相比，唯一真正的变化是我们用积分代替了求和。 | ||
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| ### 值函数迭代 | ||
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| 以及 | ||
| 在{doc}`离散情形 <mccall_model_with_sep_markov>`中，我们最终对贝尔曼算子进行迭代 |
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| 以下是一个函数，利用求得的解$v_u$来计算我们感兴趣的其余函数：$v_e$，以及延续价值函数$h$。 | ||
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| 在将求和替换为积分之后，我们使用与{doc}`离散情形 <mccall_model_with_sep_markov>`中相同的表达式。 |
| from typing import NamedTuple | ||
| from functools import partial | ||
| import quantecon as qe |
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: 2026-07-18 📝 Translation Quality
Summary: This is a high-quality, accurate translation of a technically dense document. Mathematical content, code blocks, and MyST formatting are well preserved. Terminology is consistent with the glossary and standard usage. Minor points include an unexplained 'intermediate:' prefix added to some doc cross-references (differing from the source) and a slightly restructured sentence in the Fitting section, plus additional font-setup lines in the imports cell not found in the source. These are minor issues relative to the overall strong fidelity and fluency of the translation. Mathematical notation, LaTeX expressions, and code cells are preserved accurately and completely across the whole document. Technical terminology (贴现因子, 值函数迭代, 保留工资, 蒙特卡洛积分, 横截面, etc.) is applied consistently and matches standard economics/CS Chinese conventions. Code comments within code cells are naturally and accurately translated, maintaining technical precision (e.g., '拆解模型参数', '已就业路径/失业路径'). The translation of nuanced explanatory passages (e.g., explaining fitted VFI steps, unemployment simulation logic) reads fluently and preserves logical flow from the source. Suggestions:
🔍 Diff Quality
Summary: The target document has been correctly resynced to match the current source's structure, content, and translation metadata. This review was generated automatically by action-translation review mode. |
These {doc} targets exist only in lecture-python.myst until Phase 2
translates them; qualifying with the intermediate: intersphinx prefix
gives working links now, and a future resync restores local refs once
the targets exist. Program decision recorded 2026-07-18 (Matt).
Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
…anslation#116) Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
Forward Resync: mccall_fitted_vfi.md
Source: QuantEcon/lecture-python.myst — lectures/mccall_fitted_vfi.md
Source commit:
0bfcac8This PR resyncs the translation to match the current source document.
Reason: The two documents describe entirely different implementations. The English source uses JAX, CRRA utility, a continuous Markov wage process W_t=exp(X_t) with AR(1) X_t, Monte Carlo integration with fixed Z draws, and includes extensive simulation sections (single agent dynamics, cross-sectional analysis, visualization/histograms) plus exercises on c and γ. The Chinese target uses Numba/jitclass, log utility, a lognormal iid wage draw model, different Bellman equations (bell1mcmc/bell2mcmc with scalar d), no simulation sections at all, and different exercises (varying μ of lognormal, and mean-preserving spread with uniform distribution and s). This is essentially a different (likely older) version of the lecture — the target both lacks large portions of source content (JAX code, simulation analysis, correct exercises) and contains substantial unique content (jitclass model, lognormal draws, mean-preserving spread exercise) not present in the source at all.
Changes
Whole-file resync applied. The entire document was resynced in a single pass.
Created by action-translation forward resync