HMM Course FAQ

Frequently Asked Questions

What background do I need?

You should be comfortable with:

Undergraduate probability (random variables, conditional probability, basic limit theorems)
Linear algebra (eigenvalues, eigenvectors, basic spectral theory)
Basic calculus and real analysis

Section 0 is designed to refresh measure-theoretic language just enough to make later sections precise.

Is this course focused on R code?

No. While the notation is aligned with Zucchini et al. (who use R for examples), these notes are language-agnostic. The emphasis is on:

Mathematical formulation of HMMs
Algorithms (forward–backward, Viterbi, EM) at the level of formulas and proofs
Statistical theory (consistency, asymptotic normality, identifiability)

You can implement the algorithms in any language (R, Python, Julia, C++, etc.).

How should I study using this site?

A suggested path:

Read the home page and HMM overview to understand the big picture.
Work through Sections 0–1 carefully if you are not fully comfortable with Markov chains.
Read Sections 2–3 to understand the formal HMM model and likelihood.
Spend time with Sections 4–5, doing the derivations and proofs yourself.
Use Sections 6–9 on a second pass for deeper statistical theory and advanced models.
Attempt problems from Section 11 as if they were exam questions.

Are there solutions to the problem sets?

No solutions are included here. The problems in Section 11 are intended for:

Graduate coursework and qualifying exams
Reading groups and self-study

Instructors can prepare their own solution sets or ask students to present solutions.

How long does the course take?

As a rough guide:

A 12–14 week semester course could spend 1–2 weeks per major block (Foundations, Model & Inference, Estimation, Theory, Advanced Models, Applications/Problems).
An intensive reading course could compress the material into 8–10 weeks for well-prepared students.

Can I use these notes for teaching?

Yes, subject to whatever license you choose when publishing the repository. Typical uses:

As a core set of lecture notes, supplemented with your own examples and code.
As a reading list for graduate seminars.
As background material for research students working on time-series or latent variable models.

If you use the notes in a course, consider adding a short remark in your syllabus pointing students to the site and to the primary references.

How do I report errors or suggest improvements?

See the Contact page for how to propose corrections or enhancements once the site is hosted (e.g., via GitHub issues or a simple contact form).

--- title: "HMM Course FAQ" number-sections: false --- # Frequently Asked Questions ## What background do I need? You should be comfortable with: - Undergraduate probability (random variables, conditional probability, basic limit theorems) - Linear algebra (eigenvalues, eigenvectors, basic spectral theory) - Basic calculus and real analysis Section 0 is designed to refresh **measure-theoretic language** just enough to make later sections precise. ## Is this course focused on R code? No. While the **notation** is aligned with Zucchini et al. (who use R for examples), these notes are **language-agnostic**. The emphasis is on: - Mathematical formulation of HMMs - Algorithms (forward–backward, Viterbi, EM) at the level of formulas and proofs - Statistical theory (consistency, asymptotic normality, identifiability) You can implement the algorithms in any language (R, Python, Julia, C++, etc.). ## How should I study using this site? A suggested path: 1. Read the **home page** and **HMM overview** to understand the big picture. 2. Work through **Sections 0–1** carefully if you are not fully comfortable with Markov chains. 3. Read **Sections 2–3** to understand the formal HMM model and likelihood. 4. Spend time with **Sections 4–5**, doing the derivations and proofs yourself. 5. Use **Sections 6–9** on a second pass for deeper statistical theory and advanced models. 6. Attempt problems from **Section 11** as if they were exam questions. ## Are there solutions to the problem sets? No solutions are included here. The problems in Section 11 are intended for: - Graduate coursework and qualifying exams - Reading groups and self-study Instructors can prepare their own solution sets or ask students to present solutions. ## How long does the course take? As a rough guide: - A 12–14 week semester course could spend **1–2 weeks per major block** (Foundations, Model & Inference, Estimation, Theory, Advanced Models, Applications/Problems). - An intensive reading course could compress the material into **8–10 weeks** for well-prepared students. ## Can I use these notes for teaching? Yes, subject to whatever license you choose when publishing the repository. Typical uses: - As a core set of lecture notes, supplemented with your own examples and code. - As a reading list for graduate seminars. - As background material for research students working on time-series or latent variable models. If you use the notes in a course, consider adding a short remark in your syllabus pointing students to the site and to the primary references. ## How do I report errors or suggest improvements? See the **Contact** page for how to propose corrections or enhancements once the site is hosted (e.g., via GitHub issues or a simple contact form).