Markov chain

A Markov Chain is a mathematical concept used to model a sequence of events or states, where the probability of transitioning from one state to another depends only on the current state and not on the past history. It is named after the Russian mathematician Andrey Markov, who pioneered the theory in the early 20th century.

A Markov Chain is characterized by a set of states and transition probabilities. The states represent different possible conditions or situations, and the transition probabilities define the likelihood of moving from one state to another. These probabilities are typically represented by a square matrix known as the transition matrix.

The Markov Chain operates based on the Markov property, which assumes that the future behavior of the system depends solely on its present state. This property makes Markov Chains memoryless, as the transition probabilities are determined by the current state only.

The chain progresses through a series of discrete time steps, with each step representing a transition from one state to another. The specific state at each step is determined probabilistically according to the transition probabilities. This sequential process can be repeated for any number of steps to model the system’s behavior over time.

Markov Chains have a wide range of applications in various fields, including physics, biology, economics, and computer science. They are particularly useful for modeling systems with random and sequential behavior, such as weather patterns, stock market movements, biological processes, text generation, and speech recognition.

One notable feature of Markov Chains is their ability to reach a stable state called the stationary distribution or equilibrium. In the long run, the chain settles into a steady-state where the probability of being in each state remains constant. This equilibrium distribution provides insights into the long-term behavior of the system.

Markov Chains serve as a foundation for more advanced models, such as Hidden Markov Models (HMMs) and Markov Chain Monte Carlo (MCMC) methods. These extensions have expanded the applications of Markov Chains to tasks like speech recognition, natural language processing, and Bayesian inference.

In summary, a Markov Chain is a mathematical framework used to model sequential systems, where the probability of transitioning from one state to another depends solely on the current state. With applications ranging from weather prediction to text generation, Markov Chains provide a powerful tool for understanding and analyzing dynamic processes in various domains.


Just in

Raspberry Pi is now a public company — TC

Raspberry Pi priced its IPO on the London Stock Exchange on Tuesday morning at £2.80 per share, valuing it at £542 million, or $690 million at today’s exchange rate, writes Romain Dillet. 

AlphaSense raises $650M

AlphaSense, a market intelligence and search platform, has raised $650 million in funding, co-led by Viking Global Investors and BDT & MSD Partners.

Elon Musk’s xAI raises $6B to take on OpenAI — VentureBeat

Confirming reports from April, the series B investment comes from the participation of multiple known venture capital firms and investors, including Valor Equity Partners, Vy Capital, Andreessen Horowitz (A16z), Sequoia Capital, Fidelity Management & Research Company, Prince Alwaleed Bin Talal and Kingdom Holding, writes Shubham Sharma. 

Capgemini partners with DARPA to explore quantum computing for carbon capture

Capgemini Government Solutions has launched a new initiative with the Defense Advanced Research Projects Agency (DARPA) to investigate quantum computing's potential in carbon capture.