Conditional exponential distribution and exponential evidence

In summary, the conversation discusses a problem involving a set of exponentially distributed measurements and the marginal probability of these measurements. The formula for the probability is derived and it is noted that the function may not be normalized, but there is no need for renormalization.
  • #1
Mindscrape
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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 [itex]x_n[/itex] that are exponentially distributed

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

and I know that t is exponentially distributed as

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


Homework Equations


marginal probability
[tex]p(x)=\int p(x|t) p(t) dt[/tex]


The Attempt at a Solution


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

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

If I want to find p(x) it should be
[tex]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[/tex]
[tex]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]}[/tex]
[tex]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)[/tex]

The issue is that this function isn't normalized. Are my limits wrong, or should I renormalize?
 
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  • #2
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 [itex]x_n[/itex] that are exponentially distributed

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

and I know that t is exponentially distributed as

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


Homework Equations


marginal probability
[tex]p(x)=\int p(x|t) p(t) dt[/tex]


The Attempt at a Solution


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

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

If I want to find p(x) it should be
[tex]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[/tex]
[tex]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]}[/tex]
[tex]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)[/tex]

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.
 

What is a conditional exponential distribution?

A conditional exponential distribution is a probability distribution that models the time between events in a system, given that a certain condition is met. It is often used in reliability analysis to predict the failure rate of a system, given that a specific component has already failed.

How is a conditional exponential distribution different from a regular exponential distribution?

The main difference is that a conditional exponential distribution takes into account a specific condition, whereas a regular exponential distribution assumes that all events are independent. This means that the rate parameter in a conditional exponential distribution may vary depending on the condition, while in a regular exponential distribution it remains constant.

What is exponential evidence in the context of conditional exponential distribution?

Exponential evidence refers to the likelihood that a certain event will occur within a given time frame, based on the observed data. In the context of conditional exponential distribution, it is used to calculate the probability that a system will fail within a certain time period, given the failure of a specific component.

What are some real-world applications of conditional exponential distribution?

Conditional exponential distribution is commonly used in reliability analysis of complex systems, such as manufacturing processes, transportation systems, and communication networks. It is also used in survival analysis to model the time to failure of medical devices and equipment.

What are the limitations of using conditional exponential distribution?

One limitation is that it assumes that the events in the system are independent, which may not always be the case in real-world scenarios. Additionally, it may be challenging to accurately estimate the rate parameter for the conditional distribution, especially when the condition is complex or not well-defined.

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