Conditional expectation of Exp(theta)

AI Thread Summary
To show that E(X|X ≥ τ) = τ + 1/θ for an exponentially distributed variable X, the memorylessness property is essential. This property states that P(T > s + t | T > s) = P(T > t) for all s, t ≥ 0, which can be mathematically manipulated to derive the conditional expectation. Expressing the conditional expectation as a ratio of integrals can also be effective. The discussion emphasizes the need to convert expectations into probabilities using the memorylessness property and suggests rewriting it for clarity. Understanding these concepts is crucial for successfully calculating the conditional expectation.
ghostyc
Messages
25
Reaction score
0
Given X follows an exponential distribution \theta

how could i show something like

\operatorname{E}(X|X \geq \tau)=\tau+\frac 1 \theta

?

i have get the idea of using Memorylessness property here,
but how can i combine the probabilty with the expectation?

thanks.

casper
 
Physics news on Phys.org
ghostyc said:
i have get the idea of using Memorylessness property here,
but how can i combine the probabilty with the expectation?

If you write down mathematically the Memorynessless property then it might become obvious. Otherwise it's fine to express the conditional expectation as a ratio of integrals and evaluate it directly.
 
bpet said:
If you write down mathematically the Memorynessless property then it might become obvious. Otherwise it's fine to express the conditional expectation as a ratio of integrals and evaluate it directly.

\Pr(T > s + t\; |\; T > s) = \Pr(T > t) \;\; \hbox{for all}\ s, t \ge 0.

i just can't convert from expectation to the probability...

damn
 
That's what I did so far.

But I just can't use the memoryless property to do it ...

http://img138.imageshack.us/img138/5945/tempz.jpg
 

Attachments

  • temp.jpg
    temp.jpg
    26.2 KB · Views: 539
Last edited by a moderator:
ghostyc said:
That's what I did so far.

But I just can't use the memoryless property to do it ...

Hint: rewrite the memoryless property into a form suitable to use on line 1 of your proof. Conditional probabilities -> conditional cdf -> conditional pdf.
 
I'm taking a look at intuitionistic propositional logic (IPL). Basically it exclude Double Negation Elimination (DNE) from the set of axiom schemas replacing it with Ex falso quodlibet: ⊥ → p for any proposition p (including both atomic and composite propositions). In IPL, for instance, the Law of Excluded Middle (LEM) p ∨ ¬p is no longer a theorem. My question: aside from the logic formal perspective, is IPL supposed to model/address some specific "kind of world" ? Thanks.
I was reading a Bachelor thesis on Peano Arithmetic (PA). PA has the following axioms (not including the induction schema): $$\begin{align} & (A1) ~~~~ \forall x \neg (x + 1 = 0) \nonumber \\ & (A2) ~~~~ \forall xy (x + 1 =y + 1 \to x = y) \nonumber \\ & (A3) ~~~~ \forall x (x + 0 = x) \nonumber \\ & (A4) ~~~~ \forall xy (x + (y +1) = (x + y ) + 1) \nonumber \\ & (A5) ~~~~ \forall x (x \cdot 0 = 0) \nonumber \\ & (A6) ~~~~ \forall xy (x \cdot (y + 1) = (x \cdot y) + x) \nonumber...

Similar threads

Back
Top