A Markov Chain is a stochastic model describing a sequence of possible events, in which the probability of each event depends only on the state attained in the previous event. There are many examples of Markov processes including Google's PageRank algorithm, thermodynamic models and automated speech-recognition systems.
We'll go through a sample implementation of a predictive text system like the one you can find on your phone. Then we will look at how to generate superficially real-looking words.
YOU MAY ALSO LIKE:
- A Simple Bid Recommendation Engine Using FParsec and CodeDom (SkillsCast recorded in January 2019)
- Lightbend Akka for Scala - Professional (in London on 11th - 12th November 2019)
- Advanced Scala with Dick Wall (in London on 9th - 11th December 2019)
- F# eXchange 2020 (in London on 2nd - 3rd April 2020)
- ProgNET London 2020 (in London on 16th - 18th September 2020)
- The Five Stages of Data: a holistic approach to data analytics and BI (in London on 21st October 2019)
- Code Kata: Yilin Wei - Optics with Monocle (in London on 22nd October 2019)
- Type-Safe Datatype-Generic Programming in F# (SkillsCast recorded in September 2019)
- GraphQL: May the Best API Win! (SkillsCast recorded in July 2019)
Introduction to Markov Chains in F#
Mariusz is an experienced developer on the Microsoft stack since before the dawn of the .NET Framework with a passion for programming patterns, distributed computing, machine learning algorithms and recently, functional programming and F# especially. Aspiring presenter. Book lover.