Stochastic Processes: Introduction and Tips

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Stochastic processes involve mathematical frameworks used to model systems that evolve over time with inherent randomness. Key concepts include Markov chains, random walks, and Brownian motion, which are foundational to understanding the behavior of such processes. Applications span various fields, including finance, physics, and biology, making it essential to grasp both theoretical and practical aspects. Engaging with academic papers and online courses can significantly enhance comprehension. A solid foundation in probability theory is also recommended for deeper insights into stochastic processes.
steven187
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Hi all,

Im going to be researching into Stochastic processes don't know anything about it except the title, Thought I might get on here to get an introduction, see what other people know about it and tips that would be helpful in understanding the concepts? so if anybody knows anything about stochastic Proccess please feel free to share, I will be very interested to hear it.

Thank you

Regards

Steven
 
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Hi Steven187,

are you thinking about some field of application or "just" the math of it all? Just thinking for starters since the number of "things" under stochastic processes is pretty huge.
 
Hi there,

Yeah I am pretty much focused on the maths part of it, I am going to start researching it once I get some info about it from here, let me know what you know about the topic even a global perspective or the core concepts about it would be of help.

Regards

Steven
 
The standard _A " operator" maps a Null Hypothesis Ho into a decision set { Do not reject:=1 and reject :=0}. In this sense ( HA)_A , makes no sense. Since H0, HA aren't exhaustive, can we find an alternative operator, _A' , so that ( H_A)_A' makes sense? Isn't Pearson Neyman related to this? Hope I'm making sense. Edit: I was motivated by a superficial similarity of the idea with double transposition of matrices M, with ## (M^{T})^{T}=M##, and just wanted to see if it made sense to talk...

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