y*****w
发帖数: 1350 | 1 In wiki, the binomial distribution is explained as "the discrete probability
distribution of the number of successes in a sequence of n independent yes/
no experiments, each of which yields success with probability p." I am
uncertain how to interpret that p. Again in wiki, it says "flip a coin three
times and count the number of heads. The distribution of this random number
is a binomial distribution with n = 3 and p = 1/2." However, I saw some
other examples that use an empirical group percenta | y*****w
发帖数: 1350 | | s*r
发帖数: 2757 | 3 100wb?
【在 y*****w 的大作中提到】
: 没人能给个idea?
| D******n
发帖数: 2836 | 4 p is a parameter, it can be 0 to 1
probability
yes/
three
number
【在 y*****w 的大作中提到】
: In wiki, the binomial distribution is explained as "the discrete probability
: distribution of the number of successes in a sequence of n independent yes/
: no experiments, each of which yields success with probability p." I am
: uncertain how to interpret that p. Again in wiki, it says "flip a coin three
: times and count the number of heads. The distribution of this random number
: is a binomial distribution with n = 3 and p = 1/2." However, I saw some
: other examples that use an empirical group percenta
| b*****o
发帖数: 482 | 5 你没搞清楚p和p_hat的区别. 一个是真值, 一个是对真值的估计.
wiki例子里面是assume了fair coin, 所以p=0.5. 这个时候p是真值, 不是估计出来的.
p在一般的应用中, 是你要估计的一个值. 那么这个时候MLE of p (i.e. p_hat)=#
success/N
probability
yes/
three
number
【在 y*****w 的大作中提到】
: In wiki, the binomial distribution is explained as "the discrete probability
: distribution of the number of successes in a sequence of n independent yes/
: no experiments, each of which yields success with probability p." I am
: uncertain how to interpret that p. Again in wiki, it says "flip a coin three
: times and count the number of heads. The distribution of this random number
: is a binomial distribution with n = 3 and p = 1/2." However, I saw some
: other examples that use an empirical group percenta
| y*****w
发帖数: 1350 | 6 Thanks!
的.
【在 b*****o 的大作中提到】
: 你没搞清楚p和p_hat的区别. 一个是真值, 一个是对真值的估计.
: wiki例子里面是assume了fair coin, 所以p=0.5. 这个时候p是真值, 不是估计出来的.
: p在一般的应用中, 是你要估计的一个值. 那么这个时候MLE of p (i.e. p_hat)=#
: success/N
:
: probability
: yes/
: three
: number
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