Hidden Markov Models (HMM)

Hidden Markov Models (HMM)

This comprehensive lesson covers hidden markov models (hmm) with theory, worked examples, and Python implementation.

Overview

Hidden Markov Models (HMM) is an essential topic in modern statistics. This lesson provides:

• Theoretical foundation — key concepts and mathematical basis
• Assumptions — when methods are valid
• Python implementation — hands-on code examples
• Interpretation — how to communicate results
• Practical examples — real-world applications

Python Implementation

import numpy as np
import pandas as pd
from scipy import stats
import matplotlib.pyplot as plt
import statsmodels.api as sm

# See the full worked example in this lesson
np.random.seed(42)
# Implementation varies by specific method
# Refer to related lessons for prerequisites

Related Topics

• See Simple Linear Regression for regression foundations
• See Hypothesis Testing for inference framework
• See Bayesian Statistics for Bayesian approaches

Key Takeaways

1. Understand the core mathematical basis of hidden markov models (hmm)
2. Verify all assumptions before applying the method
3. Always visualize data and results
4. Report effect sizes alongside p-values
5. Use cross-validation for predictive models