Exponential Distribution: Calculating the Half-Life & Survival Rate of a Rock"

In summary, the conversation discusses the calculation of the number of centuries that must pass before there is a 50% chance of at least one atom remaining in a piece of rock containing 10^20 atoms with an exponentially distributed lifetime. The assumption is made that the probability of survival is small and follows a Poisson distribution. Using the formula μ = ln(1020/ln 5) * (ln 2)-1, the result is approximately 65 centuries. However, the correct value for μ is ln2. This calculation can also be checked using a binomial distribution.
  • #1
Bachelier
376
0
A piece of rock contains 10^20 atoms of a particular substance. Each atom has an expoentially distributed lifetime with a half-life of one century. How many centurites must pass before

there is about a 50% chance that at least one atom remains. What assumptions are you making?

answer:

so P (at least one survives past t) = P (no one does) = .5

now, I'm making the assumption that Prob of survival is so small and since n is huge, this follows a poisson disn.

thus .5 = P(k=0) = e

then μ = ln 5

now μ = np = 1020* e-ln 2 t. ln 2 is my parameter since half time is 1 century.

thus t = ln(1020/ln 5) * (ln 2)-1 ≈ 65 years
 
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  • #2
Your time unit is centuries, not years. So your answer (I didn't check arithmetic) is 65 centuries.
 
  • #3
what about the theory, is it correct?
 
  • #4
.5 = P(k=0) = e

then μ = ln 5

Above has error, μ = -ln.5 = ln2

The general idea is correct. You might try a binomial to check. The result should be about the same.
 
  • #5


So, after approximately 65 years, there is a 50% chance that at least one atom will remain in the rock. This calculation assumes that the atoms have a constant and independent probability of decaying, and that there are no external factors affecting the decay process. It also assumes that the distribution of lifetimes of the atoms follows an exponential distribution.
 

1. What is exponential decay and how is it related to dsn?

Exponential decay is a mathematical concept that describes the rate at which a quantity decreases over time. It is related to dsn, or the digital sequence number, in the context of communication networks. In this context, dsn is used to track the sequence of packets being transmitted and received, and exponential decay is used to model the loss of packets over time.

2. How is dsn calculated and what does it represent?

Dsn is typically calculated by incrementing the previous dsn by one for each successfully transmitted packet. It represents a unique identifier for each packet, allowing for the tracking of their sequence and ensuring that they are received in the correct order.

3. What are the main factors that can affect dsn and cause exponential decay?

The main factors that can affect dsn and cause exponential decay include network congestion, packet loss, and transmission errors. These factors can disrupt the sequential transmission and reception of packets, leading to a decrease in dsn over time.

4. How does exponential decay impact the performance of a communication network?

Exponential decay can negatively impact the performance of a communication network by causing delays, retransmissions, and overall decreased efficiency. As the dsn decreases, the network may experience more errors and retransmissions, resulting in slower data transfer and reduced quality of service.

5. What are some strategies for mitigating the effects of exponential decay on dsn in communication networks?

Some strategies for mitigating the effects of exponential decay on dsn in communication networks include implementing error correction and detection techniques, using congestion control algorithms, and optimizing network infrastructure. Additionally, having a larger dsn range and implementing proper packet sequencing can also help reduce the impact of exponential decay.

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