Probability of the events that will happen $X$ times in the next $Y$ minutes.

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If an event happened at $a$, $b$ , $c$ , $d$ , $e$ .... minutes ago,
how to calculate the probability of the events that will happen $X$
times in the next $Y$ minutes?




Actually, I want to calculate the probability of the total number of corner in a soccer live match given some corners already happens.







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  • It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
    – asdf
    Aug 27 at 14:10











  • Can you improve your question by adding your thoughts and what you have tried?
    – user144410
    Aug 27 at 14:14










  • This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
    – denklo
    Aug 27 at 14:17











  • Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
    – Jan
    Aug 27 at 15:26










  • @asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
    – Kevin
    Aug 27 at 17:36















up vote
0
down vote

favorite













If an event happened at $a$, $b$ , $c$ , $d$ , $e$ .... minutes ago,
how to calculate the probability of the events that will happen $X$
times in the next $Y$ minutes?




Actually, I want to calculate the probability of the total number of corner in a soccer live match given some corners already happens.







share|cite|improve this question






















  • It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
    – asdf
    Aug 27 at 14:10











  • Can you improve your question by adding your thoughts and what you have tried?
    – user144410
    Aug 27 at 14:14










  • This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
    – denklo
    Aug 27 at 14:17











  • Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
    – Jan
    Aug 27 at 15:26










  • @asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
    – Kevin
    Aug 27 at 17:36













up vote
0
down vote

favorite









up vote
0
down vote

favorite












If an event happened at $a$, $b$ , $c$ , $d$ , $e$ .... minutes ago,
how to calculate the probability of the events that will happen $X$
times in the next $Y$ minutes?




Actually, I want to calculate the probability of the total number of corner in a soccer live match given some corners already happens.







share|cite|improve this question















If an event happened at $a$, $b$ , $c$ , $d$ , $e$ .... minutes ago,
how to calculate the probability of the events that will happen $X$
times in the next $Y$ minutes?




Actually, I want to calculate the probability of the total number of corner in a soccer live match given some corners already happens.









share|cite|improve this question













share|cite|improve this question




share|cite|improve this question








edited Aug 27 at 14:14









user144410

7461519




7461519










asked Aug 27 at 14:08









Ken Chan

1012




1012











  • It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
    – asdf
    Aug 27 at 14:10











  • Can you improve your question by adding your thoughts and what you have tried?
    – user144410
    Aug 27 at 14:14










  • This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
    – denklo
    Aug 27 at 14:17











  • Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
    – Jan
    Aug 27 at 15:26










  • @asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
    – Kevin
    Aug 27 at 17:36

















  • It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
    – asdf
    Aug 27 at 14:10











  • Can you improve your question by adding your thoughts and what you have tried?
    – user144410
    Aug 27 at 14:14










  • This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
    – denklo
    Aug 27 at 14:17











  • Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
    – Jan
    Aug 27 at 15:26










  • @asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
    – Kevin
    Aug 27 at 17:36
















It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
– asdf
Aug 27 at 14:10





It will just be $$mathbbP(textevents will happen X times in the next Y minutes| textan event happened at a, b , c , d , e .... minutes ago)$$. Without any further assumptions you can't achieve anything
– asdf
Aug 27 at 14:10













Can you improve your question by adding your thoughts and what you have tried?
– user144410
Aug 27 at 14:14




Can you improve your question by adding your thoughts and what you have tried?
– user144410
Aug 27 at 14:14












This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
– denklo
Aug 27 at 14:17





This seems quate analogue to the process of radioactive decay. You may modell the time periods of your events as being exponentially destributed, i.e. the probabilit that the next event will take longer than x minutes is $exp(-fracxlambda)$, where $lambda$ is some constant. The number of such event in a period $T$ is then Poisson-distributed. Now you may try to look for some ML estimate of parameters in question.
– denklo
Aug 27 at 14:17













Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
– Jan
Aug 27 at 15:26




Hello and welcome to Mathematics SE. To be able to answer your question we would need some information on the distribution that is underlying this. Think about it this way: How would you go about estimating the amount of corners in that timeframe? Did you consider the binomial or poisson distribution? Why is this (not) a good fit?
– Jan
Aug 27 at 15:26












@asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
– Kevin
Aug 27 at 17:36





@asdf: Probably you can "fake it" with Laplace's rule of succession or something similar? Or make up a noninformative prior and use Bayes' theorem, I suppose.
– Kevin
Aug 27 at 17:36
















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