Why is this correct - particle lifetime probability distribution

In summary: B]The conditional probability for individuals of age τ is P(t|τ) = \frac{P(t)}{\int_τ^{\infty} P(t') dt'}. (t>τ)
  • #1
ponadto
2
0
Hello,

The following problem can be found in van Kampen's "Stochastic Processes in Physics and Chemistry", Third Edition (Exercise I.3.7):



The probability distribution of lifetimes in a population is P(t). Show that the conditional probability for individuals of age τ is
\begin{equation}
P(t|τ) = \frac{P(t)}{\int_τ^{\infty} P(t') dt'} \qquad (t>τ)
\end{equation}
Note that in the case $$P(t)=\gamma e^{-\gamma t}$$ one has $$P(t|\tau)=P(t-\tau),$$ the survival chance is independent of age. Show that this is the only P for which that is true.



Now, I am only interested in the first part of the problem - the expression for P(t|τ). A solution is given here:
http://www.claudiug.com/9780444529657/chapter.php?c=1&e=38
and I find it correct.

What I do not understand is: P(t|τ) is a conditional probability density. As such it should be expressed as a fraction of two probability densities. However, the denominator in the problem above is NOT a probability density, but actual probability (that the particle has age >τ).

Where is the catch?
 
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  • #2
ponadto said:
/B] P(t|τ) is a conditional probability density. As such it should be expressed as a fraction of two probability densities.

no, why?
After all, you condition on a real probability, namely that t>τ, not on a density.
 
  • #3
Exactly, right?
But the definition says otherwise.

My point is: from the definition of probability density,
$$P_X(x) dx$$
is the probability that a random variable X has a value in [x,x+dx]. P(x) by itself is NOT the probability that the value of the random variable X will by precisely x. That is clear.

But the conditional probability density P_{X|Y}(x|y) has the following interpretation:
$$ P_{X|Y}(x|y) dx $$ is the probability that X will have a value from the set [x,x+dx], given that Y==y. Precisely equal, and not that Y will be in [y,y+dy].

And the Bayes' rule for conditional probability density is:
$$
P_{X|Y}(x|y) = \frac{P_{X,Y}(x,y)}{P_Y(y)}
$$
where all P-s, in particular P_Y, are probability densities.
(If the denominator vanishes, the conditional probability is not defined - conditions cannot be met.)
P_Y(y) is not the probability that Y==y.

And yet (going back to my original problem) I have P(τ) in the denominator, which is not a probability density, but a crude probability.

Why isn't there a probability density, that the lifetime of the particle is τ?
 

1. What is a particle lifetime probability distribution?

A particle lifetime probability distribution is a mathematical function that describes the probability of a particle existing for a certain amount of time before it decays or disappears. It is often used in particle physics to study the behavior of subatomic particles.

2. Why is it important to understand particle lifetime probability distributions?

Understanding particle lifetime probability distributions allows scientists to make predictions about the behavior of particles and study the fundamental forces that govern their existence. It also helps in the development of new technologies and advancements in fields such as medicine and energy.

3. How is a particle lifetime probability distribution determined?

A particle lifetime probability distribution is determined through experimental measurements and statistical analysis. Scientists collect data on the lifetimes of particles and use mathematical models to fit the data and determine the underlying probability distribution.

4. What factors can affect a particle's lifetime probability distribution?

The lifetime probability distribution of a particle can be influenced by various factors such as the particle's energy, interactions with other particles, and the environment it exists in. These factors can alter the probability of the particle decaying or interacting with other particles.

5. Are particle lifetime probability distributions always accurate?

While particle lifetime probability distributions are based on experimental data and mathematical models, they are not always 100% accurate. There can be uncertainties and limitations in the measurements and models used, leading to potential deviations from the predicted probability distribution. However, these distributions are still valuable tools in understanding the behavior of particles and making predictions about their lifetimes.

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