Predicting the future by ignoring the past entirely
In short: How do you model a system that repeats over time without getting bogged down by its history? This video introduces Markov Chains, a powerful statistical tool where the future depends only on the present state.
A Markov process simplifies complex systems by assuming the probability of a future state is independent of past events. While this seems like a significant restriction, it provides immense computational power for predicting trends. The video demonstrates how to visualize these transitions using diagrams and tree structures to calculate probabilities multiple steps ahead.
To ground these concepts, the presentation explores practical applications, including an analysis of 'bull' and 'bear' weeks in the stock market. It serves as a foundation for understanding how to map out shifting states in various systems.
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Sources
- Summary by Intellect · source: Intro to Markov Chains & Transition Diagrams
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