Data Skeptic
[MINI] Markov Chains
Mar 20, 2015
This podcast discusses Markov Chains and their applications in various systems including stop lights, text prediction, and bowling. The hosts explore the concept of Markov Chains in daily life and technology, as well as their impact on partially observable state spaces.
11:29
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Quick takeaways
- Markov Chains are memoryless and rely on the previous state and a random outcome to determine the current state of a system.
- Markov Chains are widely used in technology, including predictive text on smartphones, and are valuable for analyzing statistics and improving efficiency in various applications.
Deep dives
Understanding Partially Observable State Spaces and Markov Chains
In this podcast episode, the hosts discuss partially observable state spaces and their connection to Markov chains. They use examples from games like Tic-Tac-Toe and Monopoly to explain how state spaces can be described and how the current state depends on the previous state and actions taken in between. They emphasize the Markov assumption, which states that the current state only depends on the immediate previous state. They also mention how Markov chains are present in daily life experiences, such as stoplights and predictive text on smartphones.
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