Discrete-time discrete-valued random process

In summary, the random process consists of three sequences with equal probability of being chosen. It is possible to determine whether the process is i.i.d, independent increments, stationary, and wide-sense stationary by quoting the definitions of these properties and providing a yes-or-no answer for each one based on the given process.
  • #1
jandson
1
0
I have the random process:

Sequence 1: 0 0 1 0 0 1 0 0 1 0 0 1 0 0 1 ...
Sequence 2: 1 0 0 1 0 0 1 0 0 1 0 0 1 0 0 ...
Sequence 3: 0 1 0 0 1 0 0 1 0 0 1 0 0 1 0 ...

where let nature choose one of these sequences at random with equal probability 1/3.

Can we say anything about this process being i.i.d, independent increments, stationary or wide-wense stationary?
 
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  • #2
jandson said:
Can we say anything about this process being i.i.d, independent increments, stationary or wide-wense stationary?

Since you defined a specific process, you can give a definite yes-or-no answer to each of those properties.

I think you should at least quote the definitions of the properties you are interested in. Even if your post is not intended as a "homework type" question, it would make a good one.
 

FAQ: Discrete-time discrete-valued random process

1. What is a discrete-time discrete-valued random process?

A discrete-time discrete-valued random process is a type of stochastic process where the random variables take on discrete values and are observed at discrete time intervals. This means that the process is characterized by a sequence of random variables, rather than a continuous function.

2. How is a discrete-time discrete-valued random process different from other types of random processes?

A discrete-time discrete-valued random process differs from continuous-time processes in that the random variables are only observed at discrete points in time. It also differs from continuous-valued processes in that the random variables only take on discrete values, rather than a continuous range of values.

3. What is the importance of studying discrete-time discrete-valued random processes?

Discrete-time discrete-valued random processes are important in various fields such as engineering, finance, and computer science. They are used to model and analyze systems that operate in discrete time and have a finite number of possible outcomes. Understanding these processes can help in making predictions and making decisions in these fields.

4. How are discrete-time discrete-valued random processes represented and analyzed?

Discrete-time discrete-valued random processes are typically represented using probability distributions, such as the binomial or Poisson distribution. They can also be analyzed using statistical methods such as probability theory and time series analysis.

5. What are some real-world applications of discrete-time discrete-valued random processes?

Discrete-time discrete-valued random processes have many practical applications, including in stock market analysis, communication systems, and quality control. They are also used in computer science for modeling and analyzing discrete events, such as network traffic or user behavior on a website.

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