[mix_model]: Fix issues and improve the lecture #642
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hi @mmcky , I have made some changes and made sure I pushed everything to this branch. although I have checked it myself, it'll be nice to have it reviewed by you :-) if there are further issues with the lecture, please let me know! |
bishmaybarik
changed the title
bishmaybarik
changed the title
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Pull Request Overview
This PR fixes formatting, style, and link issues in the mix_model lecture to align with style guidelines and improve readability. The changes primarily involve converting raw URLs to proper markdown links, fixing capitalization in headings, and correcting a few punctuation errors.
Key Changes
- Converted raw web links to proper markdown format with descriptive text
- Fixed capitalization in section headings to follow consistent style
- Made minor punctuation and text corrections
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hi @mmcky, may I know if you have any thoughts on this? I would humbly request you to review this whenever you have the time :-) |
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Many thanks @bishmaybarik! These are great changes!
Noted that if we use
{doc}`lecture_name`
It will give use a hyperlink with the lecture title. So we can reduce the usage of "this lecture" in the lectures.
There are also a few lines of code that's touching 80-character limit so please have a go at breaking them into two lines!
Please let me know what you think.
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| Our second method uses a uniform distribution and the following fact that we also described and used in the quantecon lecture <https://python.quantecon.org/prob_matrix.html>: | ||
| Our second method uses a uniform distribution and the following fact that we also described and used in the [quantecon lecture on elementary probability with matrices](https://python.quantecon.org/prob_matrix.html): |
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| Our second method uses a uniform distribution and the following fact that we also described and used in the [quantecon lecture on elementary probability with matrices](https://python.quantecon.org/prob_matrix.html): | |
| Our second method uses a uniform distribution and the following fact that we also described and used in {doc}`prob_matrix`: |
| The reason is that now the wage sequence is actually described by a different statistical model. | ||
| The reason is that now the wage sequence is actually described by a different statistical model. | ||
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| Thus, we change the {doc}`quantecon lecture <likelihood_bayes>` specification in the following way. |
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| Thus, we change the {doc}`quantecon lecture <likelihood_bayes>` specification in the following way. | |
| Thus, we change the specification in {doc}`likelihood_bayes` in the following way. |
| each period, though the agent doesn't know the mixing parameter. | ||
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| Our first type of agent applies the learning algorithm described in {doc}`this quantecon lecture <likelihood_bayes>`. | ||
| Our first type of agent applies the learning algorithm described in {doc}`this quantecon lecture <likelihood_bayes>`. |
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| Our first type of agent applies the learning algorithm described in {doc}`this quantecon lecture <likelihood_bayes>`. | |
| Our first type of agent applies the learning algorithm described in {doc}`likelihood_bayes`. |
| Remember that our type 1 agent uses the wrong statistical model, thinking that nature mixed between $f$ and $g$ once and for all at time $-1$. | ||
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| The type 1 agent thus uses the learning algorithm studied in {doc}`this quantecon lecture <likelihood_bayes>`. | ||
| The type 1 agent thus uses the learning algorithm studied in {doc}`this quantecon lecture <likelihood_bayes>`. |
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| The type 1 agent thus uses the learning algorithm studied in {doc}`this quantecon lecture <likelihood_bayes>`. | |
| The type 1 agent thus uses the learning algorithm studied in {doc}`likelihood_bayes`. |
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| We'll assume that the person starts out with a prior probability $\pi_0(\alpha)$ on | ||
| We'll assume that the agent starts out with a prior probability $\pi_0(\alpha)$ on | ||
| $\alpha \in (0,1)$ where the prior has one of the forms that we deployed in {doc}`this quantecon lecture <bayes_nonconj>`. |
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| $\alpha \in (0,1)$ where the prior has one of the forms that we deployed in {doc}`this quantecon lecture <bayes_nonconj>`. | |
| $\alpha \in (0,1)$ where the prior has one of the forms that we deployed in {doc}`bayes_nonconj`. |
| We'll create graphs of the posterior $\pi_t(\alpha)$ as | ||
| $t \rightarrow +\infty$ corresponding to ones presented in the quantecon lecture <https://python.quantecon.org/bayes_nonconj.html>. | ||
| We'll create graphs of the posterior $\pi_t(\alpha)$ as | ||
| $t \rightarrow +\infty$ corresponding to ones presented in the [quantecon lecture on Bayesian nonconjugate priors](https://python.quantecon.org/bayes_nonconj.html). |
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| $t \rightarrow +\infty$ corresponding to ones presented in the [quantecon lecture on Bayesian nonconjugate priors](https://python.quantecon.org/bayes_nonconj.html). | |
| $t \rightarrow +\infty$ corresponding to ones presented in {doc}`bayes_nonconj`. |
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| A compound lottery can be said to create a _mixture distribution_. | ||
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| Our two ways of constructing a compound lottery will differ in their **timing**. |
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This might be out of the scope of this PR but we should only use bold for definitions
| Our two ways of constructing a compound lottery will differ in their **timing**. | |
| Our two ways of constructing a compound lottery will differ in their *timing*. |
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| $$ | ||
| \pi_t = E [ \textrm{nature chose distribution} f | w^t] , \quad t = 0, 1, 2, \ldots | ||
| \pi_t = E [ \textrm{nature chose distribution} f | w^t] , \quad t = 0, 1, 2, \ldots |
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Adding a space between
| \pi_t = E [ \textrm{nature chose distribution} f | w^t] , \quad t = 0, 1, 2, \ldots | |
| \pi_t = E [ \textrm{nature chose distribution } f | w^t] , \quad t = 0, 1, 2, \ldots |
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| See <https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html> for the | ||
| `searchsorted` function. | ||
| See the [numpy.searchsorted documentation](https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html) for details on the `searchsorted` function. |
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| See the [numpy.searchsorted documentation](https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html) for details on the `searchsorted` function. | |
| See the [`numpy.searchsorted` documentation](https://numpy.org/doc/stable/reference/generated/numpy.searchsorted.html) for details on the `searchsorted` function. |
2 participants
This PR fixes the following issues:
The issue was the following:
link text,doclinks.