About this episode
This episode introduces the idea of a Markov Chain. A Markov Chain has a set of states describing a particular system, and a probability of moving from one state to another along every valid connected state. Markov Chains are memoryless, meaning they don't rely on a long history of previous observations. The current state of a system depends only on the previous state and the results of a random outcome.
Markov Chains are a useful way method for describing non-deterministic systems. They are useful for destribing the state and transition model of a stochastic system.
As examples of Markov Chains, we discuss stop light signals, bowling, and text prediction systems in light of whether or not they can be described with Markov Chains.
Get every episode summarized
Each time Data Skeptic publishes, we email you a written briefing from the transcript — the topics, who appeared, and any specific claims, with the ad reads skipped.
Email me new episodesFree for 3 shows. No card needed.
Hosts & guests
No transcript yet
This episode has not been transcribed. Request it and it moves to the front of the queue.
More episodes
