Applied Stochastic Processess?

This is not a distribution.In summary, the random variable X, which follows a Gaussian distribution with parameters μ and σ, has a first moment of m, a second moment of σ^2 m^2, and a third moment of 0. When transformed by adding b, it follows a Gaussian distribution with parameters m b and σ. When multiplied by σ, it also follows a Gaussian distribution with parameters σm and σ^2. Finally, when squared, it follows a Chi-squared distribution law. The exact distribution of X^2 depends on the values of μ and σ.
  • #1
ra_forever8
129
0
Random variable X is distributed according to Gaussian distribution
P(x)= (1/sqrt(2πσ^2) * exp(- (x-μ)^2 / (2σ^2) )
1. Calculate its first three moments
2. What are the distributions of Y= X+b; Z=σX; W=X^2


P(x)= (1/sqrt(2πσ^2) * exp(- (x-μ)^2 / (2σ^2) )
so
1.1.
the moment of first degree is:
E(X) = Int (on |R of) x dP(x) = m

1.2. moment of second degree:
we have Var(X) = σ^2 = E(X^2) - E(X)^2 = E(X^2) - m^2
so the moment of degree 2 is
E(X^2) = σ^2 m^2

1.3.moment of third order :
as P is an even function :
E(X^3) = Int ( on |R of) x^3 P(x) dx = 0
and it 's the case for any odd moment n : n = 2k 1

2.
the gaussian characteristic is stable through linear transformation :
Y = X b is gaussian
ith the same σ
and with an expectence of E(Y) = m b
so Y is a N(m b ; σ)

Z = σX is gaussian too :
E(Z) = σm and Var(Z) = σ^2 Var(X) = σ^4
Z is therefore a N(σm , σ^2) (σ^2 is its standard deviation

W = X^2 follws a Khi-squared distribution law ;


CAN SOMEONE CHECK MY METHODS AND HELP ME TO DO LAST QUESTION W=X^2. THANK YOU
 
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  • #2
ra_forever8 said:
Random variable X is distributed according to Gaussian distribution
P(x)= (1/sqrt(2πσ^2) * exp(- (x-μ)^2 / (2σ^2) )
1. Calculate its first three moments
2. What are the distributions of Y= X+b; Z=σX; W=X^2


P(x)= (1/sqrt(2πσ^2) * exp(- (x-μ)^2 / (2σ^2) )
so
1.1.
the moment of first degree is:
E(X) = Int (on |R of) x dP(x) = m

1.2. moment of second degree:
we have Var(X) = σ^2 = E(X^2) - E(X)^2 = E(X^2) - m^2
so the moment of degree 2 is
E(X^2) = σ^2 m^2

1.3.moment of third order :
as P is an even function :
E(X^3) = Int ( on |R of) x^3 P(x) dx = 0
and it 's the case for any odd moment n : n = 2k 1

2.
the gaussian characteristic is stable through linear transformation :
Y = X b is gaussian
ith the same σ
and with an expectence of E(Y) = m b
so Y is a N(m b ; σ)

Z = σX is gaussian too :
E(Z) = σm and Var(Z) = σ^2 Var(X) = σ^4
Z is therefore a N(σm , σ^2) (σ^2 is its standard deviation

W = X^2 follws a Khi-squared distribution law ;


CAN SOMEONE CHECK MY METHODS AND HELP ME TO DO LAST QUESTION W=X^2. THANK YOU

1.1. Determine the value of m, in terms of μ and σ.
1.2. From σ^2 = E(X^2) - (E X)^2 we do NOT get E(X^2) = σ^2 * (E X)^2, which is what you wrote. Did you mean that, or was it a typo?
1.3. P(x) is most definitely not an even function if μ ≠ 0. Start over.

I don't know what you are doing in 2. You were asked about X+b but instead, you told us about Xb. Are you sure you were asked about σX? A more meaningful question would be about (1/σ)X. As for X^2: just apply the standard formulas you find in your textbook or course notes. It might be quite complicated; much easier would be the case of X^2 if we had μ = 0.
 

FAQ: Applied Stochastic Processess?

What is the definition of Applied Stochastic Processes?

Applied Stochastic Processes is a branch of mathematics that studies random phenomena and their applications in various fields, such as finance, engineering, and biology. It involves using mathematical models and tools to understand and predict the behavior of systems that are subject to random variations.

What are some examples of Applied Stochastic Processes?

Some examples of Applied Stochastic Processes include stock market fluctuations, weather patterns, traffic flow, and disease spread. These processes are characterized by randomness and uncertainty, making them suitable for analysis using stochastic models.

How is Applied Stochastic Processes different from regular Stochastic Processes?

Applied Stochastic Processes differs from regular Stochastic Processes in that it focuses on real-world applications and practical solutions. Regular Stochastic Processes are more theoretical and may not have direct applications in the real world. Applied Stochastic Processes also often deals with complex systems and incorporates data from various fields.

What are some common techniques used in Applied Stochastic Processes?

Some common techniques used in Applied Stochastic Processes include Markov chains, queuing theory, Brownian motion, Monte Carlo simulations, and time series analysis. These techniques allow for the analysis and prediction of random processes and their impact on systems.

What are the benefits of studying Applied Stochastic Processes?

Studying Applied Stochastic Processes can provide a deeper understanding of how random phenomena affect various systems and how to make predictions and decisions based on this information. It is also a valuable skill for many industries, such as finance and data analysis, as it allows for better risk management and decision-making in uncertain situations.

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