Information Theory- DMS question- Binomial dist?

In summary, the proportion of messages in the typical set converges to zero as n approaches infinity, unless the messages are uniformly distributed on the set A.
  • #1
Zoe-b
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Homework Statement


Let X1, . . . ,Xn be a message from a memoryless source, where Xi are in A. Show
that, as n →∞, the proportion of messages in the typical set converges to zero,
unless Xi is uniform on A.


Homework Equations





The Attempt at a Solution


Confused, possibly because I'm reading the question wrong.
Let B be a 'typical set' (proper subset of A), with P(Xi in B) = p

Then as far as I can tell, if Yn is the number of messages in B up to Xn, Yn has a Binomial (n,p) distribution and so the proportion of messages in B tends to p not to zero! But I'm not using how the actual 'letters' are distributed at all here, or the respective sizes of the sets A, B. Any hints?
 
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  • #2


Hello,

Thank you for your response. It seems like you are on the right track, but there are a few key concepts that you may have missed. First, it is important to note that the message from a memoryless source means that each message is independent and identically distributed, which means that each message has the same probability distribution. Second, the typical set is defined as a set of messages that are "typical" for the source, meaning that they are representative of the overall distribution of messages.

Now, let's consider the proportion of messages in the typical set as n approaches infinity. As you correctly stated, Yn has a Binomial (n,p) distribution, where p is the probability of a message being in the typical set. As n approaches infinity, the Binomial distribution approaches a Poisson distribution, with parameter λ = np. This means that the proportion of messages in the typical set can be approximated by P(Yn = 0) = e^(-λ), which approaches zero as n approaches infinity.

So, in conclusion, as n approaches infinity, the proportion of messages in the typical set converges to zero unless the probability of a message being in the typical set (p) is equal to 1, which would only be the case if Xi is uniform on A. I hope this clarifies things for you. Let me know if you have any further questions.
 

1. What is Information Theory and why is it important?

Information Theory is a mathematical theory that deals with the quantification, storage, and communication of information. It helps us understand how information is processed and transmitted, and how to maximize efficiency and minimize errors in communication systems. It has applications in a wide range of fields, including computer science, telecommunications, genetics, and neuroscience.

2. What is DMS (Discrete Memoryless Source) in Information Theory?

DMS, or Discrete Memoryless Source, is a fundamental concept in Information Theory. It refers to a source that produces a sequence of symbols or messages, where each symbol is independent and has a fixed probability of occurrence. This type of source is useful for modeling many real-world systems, such as communication channels and genetic sequences.

3. What is a Binomial Distribution in Information Theory?

A Binomial Distribution is a probability distribution that describes the possible outcomes of a series of independent trials, where each trial has only two possible outcomes (usually labeled as "success" or "failure"). It is commonly used in Information Theory to model the probability of a certain number of successful transmissions in a communication system.

4. How is Information Theory used in data compression?

Information Theory plays a crucial role in data compression, which is the process of reducing the size of data without losing any information. By understanding the fundamental limits of information transmission and storage, we can develop more efficient compression algorithms that can compress data while preserving its content.

5. Can Information Theory be applied to non-binary systems?

Yes, Information Theory can be applied to non-binary systems. While the theory was initially developed for binary systems (i.e. systems with only two possible values), it has since been extended to handle systems with multiple possible values, such as ternary, quaternary, and continuous systems. This allows us to analyze and optimize a wide range of systems, including those used in digital communication, wireless networks, and signal processing.

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