Markov Processes are stochastic 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. These processes are fundamental in various fields such as economics, genetics, and computer science for modeling random systems that evolve over time.

Markov Processes

Stochastic models are mathematical frameworks used to represent systems or processes that are inherently random, allowing for the analysis and prediction of outcomes that are not deterministic. They are widely used in fields such as finance, physics, biology, and engineering to model uncertainty and variability in complex systems.

stochastic models

sequence of possible events

The Markov property is a fundamental characteristic of stochastic processes, where the future state of the process depends only on the present state and not on the sequence of events that preceded it. This memoryless property simplifies the analysis and modeling of complex systems in fields like physics, economics, and artificial intelligence.

Markov property

Fundamental Processes

Economics is the study of how individuals, businesses, and governments make choices on allocating resources to satisfy their wants and needs, and how these choices interact to shape markets and economies. It encompasses both microeconomic and macroeconomic perspectives, analyzing everything from individual consumer behavior to global economic trends.

economics

Genetics is the study of heredity and variation in living organisms, focusing on how traits are passed from parents to offspring through genes. It encompasses the molecular structure and function of genes, gene behavior in the context of a cell or organism, and patterns of inheritance across generations.

genetics

Computer science is the study of algorithmic processes, computational machines, and computation itself, which underpins the development of software and systems that drive modern technology. It encompasses a wide range of topics from theoretical foundations to practical applications, making it essential for innovation in various fields such as artificial intelligence, cybersecurity, and data science.

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