Expected Value and Uniform Distribution

Oct 13, 2025 by sorusgentrification.com 22 views

Problem Description

As I was playing around with expected number problems regarding particularly strings $[0,1]^\mathbb{N}$ , I stumbled upon this problem that I couldn't solve. Suppose $r=0.a_0 a_1 \dots$ is selected uniformly at random from the interval $[0,1]$ . We are interested in finding the expected first occurrence of the subdecimal of $r$ being greater than $r$ .

Understanding the Problem

To tackle this problem, we need to understand the concept of subdecimals and how they relate to the given interval $[0,1]$ . A subdecimal of a number $r$ is a sequence of digits that appears within the decimal representation of $r$ . For instance, if $r=0.123456\dots$ , then the subdecimals of $r$ are $0.1$ , $0.12$ , $0.123$ , and so on.

Expected Value and Uniform Distribution

The problem states that $r$ is selected uniformly at random from the interval $[0,1]$ . This means that every possible value of $r$ has an equal probability of being selected. In other words, the probability density function of $r$ is a uniform distribution over the interval $[0,1]$ .

Expected First Occurrence

The expected first occurrence of the subdecimal of $r$ being greater than $r$ can be thought of as the expected value of the first position at which the subdecimal of $r$ exceeds the value of $r$ . To calculate this, we need to consider the probability of the subdecimal of $r$ being greater than $r$ at each position.

Calculating the Probability

Let's denote the probability of the subdecimal of $r$ being greater than $r$ at position $n$ as $P_n$ . We can calculate $P_n$ by considering the possible values of the first $n$ digits of $r$ . If the first $n$ digits of $r$ are $0.a_0 a_1 \dots a_{n-1}$ , then the subdecimal of $r$ at position $n$ is $0.a_0 a_1 \dots a_{n-1} a_n$ .

Recursive Relationship

We can establish a recursive relationship between $P_n$ and $P_{n-1}$ . If the first $n-1$ digits of $r$ are $0.a_0 a_1 \dots a_{n-2}$ , then the subdecimal of $r$ at position $n-1$ is $0.a_0 a_1 \dots a_{n-2}$ . If $a_{n-1} < a_{n-2}$ , then the subdecimal of $r$ at position $n$ is greater than $r$ if and only if the subdecimal of $r$ at position $n-1$ is greater than $r$ . On the other hand, if $a_{n-1} \geq a_{n-2}$ , then the subdecimal of $r$ at position $n$ is greater than $r$ if and only if the subdecimal of $r$ at position $n-1$ is greater than $r$ and $a_n > a_{n-1}$ .

Probability of Subdecimal Being Greater Than $r$

Using the recursive relationship established above, we can calculate the probability of the subdecimal of $r$ being greater than $r$ at position $n$ as follows:

P_n = \frac{1}{10} \sum_{i=0}^9 P_{n-1} + \frac{1}{10} \sum_{i=0}^9 P_{n-1} \cdot \frac{i}{10}

Expected First Occurrence

The expected first occurrence of the subdecimal of $r$ being greater than $r$ can be calculated as follows:

E = \sum_{n=1}^\infty n \cdot P_n

Simplifying the Expression

Using the recursive relationship established above, we can simplify the expression for $E$ as follows:

E = \sum_{n=1}^\infty n \cdot \left( \frac{1}{10} \sum_{i=0}^9 P_{n-1} + \frac{1}{10} \sum_{i=0}^9 P_{n-1} \cdot \frac{i}{10} \right)

Calculating the Sum

To calculate the sum, we can use the following formula:

\sum_{n=1}^\infty n \cdot x^n = \frac{x}{(1-x)^2}

Expected First Occurrence

Using the formula above, we can calculate the expected first occurrence of the subdecimal of $r$ being greater than $r$ as follows:

E = \frac{1}{10} \sum_{i=0}^9 \frac{1}{(1-1/10)^2} + \frac{1}{10} \sum_{i=0}^9 \frac{i/10}{(1-1/10)^2}

Simplifying the Expression

Using the formula above, we can simplify the expression for $E$ as follows:

E = \frac{1}{10} \cdot \frac{1}{(1-1/10)^2} + \frac{1}{10} \cdot \frac{1/10}{(1-1/10)^2}

Final Answer

The expected first occurrence of the subdecimal of $r$ being greater than $r$ is:

E = \frac{1}{10} \cdot \frac{1}{(1-1/10)^2} + \frac{1}{10} \cdot \frac{1/10}{(1-1/10)^2}

E = \frac{1}{10} \cdot \frac{1}{(9/10)^2} + \frac{1}{10} \cdot \frac{1/10}{(9/10)^2}

E = \frac{1}{10} \cdot \frac{100}{81} + \frac{1}{10} \cdot \frac{1}{10} \cdot \frac{100}{81}

E = \frac{10}{81} + \frac{1}{81}

E = \frac{11}{81}

Q: What is the expected first occurrence of the subdecimal of $r$ being greater than $r$ ?

A: The expected first occurrence of the subdecimal of $r$ being greater than $r$ is $\boxed{\frac{11}{81}}$ .

Q: How was the expected first occurrence calculated?

A: The expected first occurrence was calculated using the recursive relationship between the probability of the subdecimal of $r$ being greater than $r$ at position $n$ and the probability of the subdecimal of $r$ being greater than $r$ at position $n-1$ . The recursive relationship was then used to simplify the expression for the expected first occurrence.

Q: What is the significance of the uniform distribution of $r$ ?

A: The uniform distribution of $r$ is crucial in calculating the expected first occurrence. The uniform distribution ensures that every possible value of $r$ has an equal probability of being selected, which allows us to calculate the expected first occurrence using the recursive relationship.

Q: How does the expected first occurrence relate to the concept of subdecimals?

A: The expected first occurrence is directly related to the concept of subdecimals. The expected first occurrence is the expected value of the first position at which the subdecimal of $r$ exceeds the value of $r$ .

Q: Can the expected first occurrence be calculated for other types of distributions?

A: Yes, the expected first occurrence can be calculated for other types of distributions. However, the calculation would be more complex and would require a different approach.

Q: What are some potential applications of the expected first occurrence?

A: The expected first occurrence has potential applications in various fields, such as probability theory, statistics, and computer science. It can be used to model and analyze complex systems, and to make predictions about the behavior of these systems.

Q: How can the expected first occurrence be used in real-world scenarios?

A: The expected first occurrence can be used in real-world scenarios such as:

Modeling the behavior of complex systems, such as financial markets or social networks
Analyzing the performance of algorithms and data structures
Making predictions about the behavior of systems under different conditions

Q: What are some potential limitations of the expected first occurrence?

A: Some potential limitations of the expected first occurrence include:

The calculation of the expected first occurrence assumes a uniform distribution of $r$ , which may not be the case in real-world scenarios
The expected first occurrence may not be applicable to systems with non-linear behavior
The calculation of the expected first occurrence may be computationally intensive for large systems

Q: How can the expected first occurrence be extended to more complex systems?

A: The expected first occurrence can be extended to more complex systems by using advanced mathematical techniques, such as stochastic processes and Markov chains. Additionally, the expected first occurrence can be approximated using numerical methods, such as Monte Carlo simulations.

Q: What are some potential future directions for research on the expected first occurrence?

A: Some potential future directions for research on the expected first occurrence include:

Developing more efficient algorithms for calculating the expected first occurrence
Investigating the behavior of the expected first occurrence in non-linear systems
Applying the expected first occurrence to real-world scenarios, such as finance and social networks.