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:
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 17.2-184.108.40.206, 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.
- create concept: shift + click on graph
- change concept title: shift + click on existing concept
- link together concepts: shift + click drag from one concept to another
- remove concept from graph: click on concept then press delete/backspace
- add associated content to concept: click the small circle that appears on the node when hovering over it
- other actions: use the icons in the upper right corner to optimize the graph placement, preview the graph, or download a json representation