Conditional exponential distribution and exponential evidence

Mindscrape
Messages
1,854
Reaction score
1

Homework Statement


This is a subset of a larger problem I'm working on, but once I get over this hang up I should be good to go. I have a set of measurements x_n that are exponentially distributed

p(x_n|t)=e^{-(x_n-t)} I_{[x_n \ge t]}

and I know that t is exponentially distributed as

p(t)=e^{-t}I_{[t\ge0]}


Homework Equations


marginal probability
p(x)=\int p(x|t) p(t) dt


The Attempt at a Solution


So the probability of N observations of x are
p(\mathbf{x}|t)=e^{-s(x)} e^{Nt} I_{[\textrm{min}(x_n) \ge t]}
where
s(x)=\sum_{n=1}^N x_n

Which means that
p(\mathbf{x},t)=e^{-s(x)} e^{t(N-1)} I_{[\textrm{min}(x_n) \ge t]} I_{[t\ge0]}

If I want to find p(x) it should be
p(\mathbf{x})=\int_0^{x_{min}} e^{-s(x)}e^{t(N-1)} I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]} dt
p(\mathbf{x})=e^{-s(x)}\frac{1}{N-1}e^{t(N-1)}|^{t=x_{min}}_{t=0}I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]}
p(\mathbf{x})=e^{-s(x)}\frac{1}{N-1}I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]}(e^{x_{min}(N-1)}-1)

The issue is that this function isn't normalized. Are my limits wrong, or should I renormalize?
 
Last edited:
Physics news on Phys.org
Mindscrape said:

Homework Statement


This is a subset of a larger problem I'm working on, but once I get over this hang up I should be good to go. I have a set of measurements x_n that are exponentially distributed

p(x_n|t)=e^{-(x_n-t)} I_{[x_n \ge t]}

and I know that t is exponentially distributed as

p(t)=e^{-t}I_{[t\ge0]}


Homework Equations


marginal probability
p(x)=\int p(x|t) p(t) dt


The Attempt at a Solution


So the probability of N observations of x are
p(\mathbf{x}|t)=e^{-s(x)} e^{Nt} I_{[\textrm{min}(x_n) \ge t]}
where
s(x)=\sum_{n=1}^N x_n

Which means that
p(\mathbf{x},t)=e^{-s(x)} e^{t(N-1)} I_{[\textrm{min}(x_n) \ge t]} I_{[t\ge0]}

If I want to find p(x) it should be
p(\mathbf{x})=\int_0^{x_{min}} e^{-s(x)}e^{t(N-1)} I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]} dt
p(\mathbf{x})=e^{-s(x)}\frac{1}{N-1}e^{t(N-1)}|^{t=x_{min}}_{t=0}I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]}
p(\mathbf{x})=e^{-s(x)}\frac{1}{N-1}I_{[\textrm{min}(x_n) \ge t]}I_{[t\ge0]}(e^{x_{min}(N-1)}-1)

The issue is that this function isn't normalized. Are my limits wrong, or should I renormalize?

The formula for ##p(\mathbf{x})## should not have ##t## in it.

Anyway, why would you need to re-normalize? Your ##p(\mathbf{x})## integrates to 1 when integrated over ##\mathbb{R}_{+}^N##. If you don't believe it, try the simple cases of N = 2 and N = 3 first.
 
There are two things I don't understand about this problem. First, when finding the nth root of a number, there should in theory be n solutions. However, the formula produces n+1 roots. Here is how. The first root is simply ##\left(r\right)^{\left(\frac{1}{n}\right)}##. Then you multiply this first root by n additional expressions given by the formula, as you go through k=0,1,...n-1. So you end up with n+1 roots, which cannot be correct. Let me illustrate what I mean. For this...
Back
Top