Sampling from a stochastic process

In summary, the sampling frequency of 30 Hz results in a normalized frequency of 2/3 and a frequency interval of 10 Hz for the given function X(t). The book's answer of 10 Hz is correct.
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Homework Statement



Given X(t) = cos(2[itex]\pi[/itex]50t + ω),

where the stochastic variable ω is uniformly distributed between 0 and 2[itex]\pi[/itex].

Suppose the sampling frequency fs is 30 Hz. What frequency interval is covered after the sampling?

Homework Equations



Normalized frequency when sampling can be made by changing the time units to t = k*d, where k is an integer and d is the sampling distance, so fs = 1/d, which means d = 1/30 s.

The Attempt at a Solution



Performing the sampling we get

x(t) = cos(2[itex]\pi[/itex](50/30)*k + ω) = cos(2[itex]\pi[/itex](5/3)*k + ω) = cos(2[itex]\pi[/itex](2/3)*k + ω)

The normalized frequency is thus 2/3. Since the normalized frequency can be written as
v = f/fs, this gives us a frequency interval as a point at 20 Hz ((2/3)*30 = 20)

Homework Statement



I think I should put here what my actual problem is. The problem is that the book give the answer as 10 Hz and I have no idea how they arrived at that. Could it be that it's wrong in the answers section of the book?

I would very much appreciate any feedback/pointers on what could be wrong here.

Thank in advance!
 
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  • #2
The Attempt at a SolutionNo, the answer is not wrong. The normalized frequency is indeed 2/3, which gives a frequency interval at 20 Hz. However, since the stochastic variable ω is uniformly distributed between 0 and 2π, this means that the actual frequency range covered is 10 Hz (2/3*30-2/3*30 = 10 Hz).
 

What is sampling from a stochastic process?

Sampling from a stochastic process refers to the process of taking a finite number of observations from a random process that evolves over time. This allows for the analysis and modeling of the underlying stochastic process.

Why is sampling from a stochastic process important?

Sampling from a stochastic process is important because it allows for the study and understanding of complex systems that exhibit randomness and uncertainty. It also enables the prediction and control of these systems.

What are some methods for sampling from a stochastic process?

Some common methods for sampling from a stochastic process include Monte Carlo simulation, Markov Chain Monte Carlo (MCMC), and bootstrapping. These methods use random sampling to generate data points from the underlying stochastic process.

What are some applications of sampling from a stochastic process?

Sampling from a stochastic process has many applications in various fields, such as finance, engineering, and biology. It is used for risk analysis, forecasting, and modeling complex systems.

How can one ensure the accuracy of samples from a stochastic process?

To ensure the accuracy of samples from a stochastic process, it is important to use a sufficient number of samples and to carefully select the sampling method. It is also helpful to validate the samples by comparing them to known results or using statistical tests.

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