Markov Models
Markov Models are probabilistic models that describe a sequence of possible events where the probability of each event depends only on the state attained in the previous event, known as the Markov property. They are widely used in fields like natural language processing, speech recognition, and bioinformatics to model stochastic processes and predict future states based on current information. Common types include Markov chains, hidden Markov models (HMMs), and Markov decision processes (MDPs).
Developers should learn Markov Models when working on projects involving sequential data analysis, prediction, or pattern recognition, such as text generation, part-of-speech tagging, or financial forecasting. They are essential for building systems that need to model dependencies over time without requiring extensive historical context, making them efficient for real-time applications and machine learning tasks where memory and computational resources are constrained.