Markov models are a kind of probabilistic model often used in language modeling. The observations are assumed to follow a Markov chain, where each observation is independent of all past observations given the previous one.
This concept has the prerequisites:
- Markov chains (Markov models are based on Markov chains.)
- matrix multiplication (Markov models can be conveniently represented using transition matrices.)
- conditional distributions (Markov models are defined in terms of the transition distribution, which is a conditional distribution.)
- conditional independence (Markov models can be defined in terms of a conditional independence property.)
Core resources (we're sorry, we haven't finished tracking down resources for this concept yet)
Supplemental resources (the following are optional, but you may find them useful)
→ Pattern Recognition and Machine Learning
A textbook for a graduate machine learning course, with a focus on Bayesian methods.
Location: Section 13.1, pages 607-610
→ Machine Learning: a Probabilistic Perspective
A very comprehensive graudate-level machine learning textbook.
Location: Sections 17.2-188.8.131.52, pages 589-593
- N-gram language models are a commonly used application of Markov models, where the goal is to model the distribution over text.
- Hidden Markov models are a widely used class of probabilistic models where the data are explained in terms of a latent Markov chain.
- Brownian motion is a kind of continuous Markov model.
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