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Complex Variables:Analytic functions, integration, power series, residues, and conformal mapping.

Elementary Stochastic Processes: Markov chains, Poisson process, and Brownian motion.

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- Thread starter casesam
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In summary, the conversation is about a math and physics major who wants to pursue biophysics in grad school. They are undecided on which math course to take - Complex Variables or Elementary Stochastic Processes. Both courses have brief descriptions, with Complex Variables covering analytic functions, integration, power series, residues, and conformal mapping, and Elementary Stochastic Processes covering Markov chains, Poisson process, and Brownian motion. The group ultimately agrees that Stochastic Processes would be more useful for practical applications in biology, but also acknowledges the importance and fascination of Complex Variables.

- #1

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Complex Variables:Analytic functions, integration, power series, residues, and conformal mapping.

Elementary Stochastic Processes: Markov chains, Poisson process, and Brownian motion.

Thank You!

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- #2

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deluks917 said:I vote stochastic Processes.

Agree.

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deluks917 said:

chingkui said:Agree.

Although I hate the thought of a mathematician not taking complex analysis, I have to concur. But I echo deluks in that you

Complex variables are numbers that have both a real and imaginary component. They are represented in the form a + bi, where a is the real part and bi is the imaginary part. Complex variables are used in various branches of mathematics and physics, such as complex analysis and quantum mechanics.

Complex variables have many applications in mathematics, physics, and engineering. Some common applications include solving differential equations, analyzing electrical circuits, and studying fluid flow and aerodynamics.

A stochastic process is a mathematical model that describes the evolution of a system over time, taking into account random variations. A random process, on the other hand, is a sequence of random variables that represent a system's behavior over time. In other words, a random process is a specific instance of a stochastic process.

Stochastic processes are used in a wide range of real-world applications, including finance, economics, biology, engineering, and telecommunications. They are often used to model and analyze systems with random behavior, such as stock prices, weather patterns, and genetic mutations.

Some common types of stochastic processes include Markov processes, Poisson processes, and Gaussian processes. Markov processes describe systems with a finite number of states and random transitions between them. Poisson processes model events that occur randomly over time. Gaussian processes are used to model systems with continuous random variables and are often used in machine learning and data analysis.

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