probability from mean and std in bounded set
We have discrete and bounded data. From a to b, mean c, and std d
So how can I compute the probability at each discrete point, from the std and mean
This is the so far work, but works for 'heads' or tails only or p 0.5 for each event, eg computes the probability of getting 3 right out of 8, and 4 right out of 8 and 8 right out of that many coin tosses. Hence how to insert mean and std? For example you have 10 events and predict right 4,10,10,4 of those in 4 attempts. Mean is 7, std 3, so how to get p(8)?
from __future__ import division
def powersetCardinale(members_):
return (2 ** members_)
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n - 1)
def factorialQuicker(n, r):
if n == r:
return 1
else:
return n * factorialQuicker(n - 1, r)
def combination(n, r):
if (r == 0): return 1
if (n < r): return 0
return (factorialQuicker(n, r) / factorial(n - r))
k = 33
values = range(k + 1)
prob = values
for i in range(len(prob)):
prob[i] = combination(len(prob)-1 , prob[i]) / powersetCardinale(k)
sum(prob)
plt.plot(prob)
print(prob)
numpy scipy standard-deviation probability-density
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
add a comment |
We have discrete and bounded data. From a to b, mean c, and std d
So how can I compute the probability at each discrete point, from the std and mean
This is the so far work, but works for 'heads' or tails only or p 0.5 for each event, eg computes the probability of getting 3 right out of 8, and 4 right out of 8 and 8 right out of that many coin tosses. Hence how to insert mean and std? For example you have 10 events and predict right 4,10,10,4 of those in 4 attempts. Mean is 7, std 3, so how to get p(8)?
from __future__ import division
def powersetCardinale(members_):
return (2 ** members_)
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n - 1)
def factorialQuicker(n, r):
if n == r:
return 1
else:
return n * factorialQuicker(n - 1, r)
def combination(n, r):
if (r == 0): return 1
if (n < r): return 0
return (factorialQuicker(n, r) / factorial(n - r))
k = 33
values = range(k + 1)
prob = values
for i in range(len(prob)):
prob[i] = combination(len(prob)-1 , prob[i]) / powersetCardinale(k)
sum(prob)
plt.plot(prob)
print(prob)
numpy scipy standard-deviation probability-density
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
add a comment |
We have discrete and bounded data. From a to b, mean c, and std d
So how can I compute the probability at each discrete point, from the std and mean
This is the so far work, but works for 'heads' or tails only or p 0.5 for each event, eg computes the probability of getting 3 right out of 8, and 4 right out of 8 and 8 right out of that many coin tosses. Hence how to insert mean and std? For example you have 10 events and predict right 4,10,10,4 of those in 4 attempts. Mean is 7, std 3, so how to get p(8)?
from __future__ import division
def powersetCardinale(members_):
return (2 ** members_)
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n - 1)
def factorialQuicker(n, r):
if n == r:
return 1
else:
return n * factorialQuicker(n - 1, r)
def combination(n, r):
if (r == 0): return 1
if (n < r): return 0
return (factorialQuicker(n, r) / factorial(n - r))
k = 33
values = range(k + 1)
prob = values
for i in range(len(prob)):
prob[i] = combination(len(prob)-1 , prob[i]) / powersetCardinale(k)
sum(prob)
plt.plot(prob)
print(prob)
numpy scipy standard-deviation probability-density
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
We have discrete and bounded data. From a to b, mean c, and std d
So how can I compute the probability at each discrete point, from the std and mean
This is the so far work, but works for 'heads' or tails only or p 0.5 for each event, eg computes the probability of getting 3 right out of 8, and 4 right out of 8 and 8 right out of that many coin tosses. Hence how to insert mean and std? For example you have 10 events and predict right 4,10,10,4 of those in 4 attempts. Mean is 7, std 3, so how to get p(8)?
from __future__ import division
def powersetCardinale(members_):
return (2 ** members_)
def factorial(n):
if n == 0:
return 1
else:
return n * factorial(n - 1)
def factorialQuicker(n, r):
if n == r:
return 1
else:
return n * factorialQuicker(n - 1, r)
def combination(n, r):
if (r == 0): return 1
if (n < r): return 0
return (factorialQuicker(n, r) / factorial(n - r))
k = 33
values = range(k + 1)
prob = values
for i in range(len(prob)):
prob[i] = combination(len(prob)-1 , prob[i]) / powersetCardinale(k)
sum(prob)
plt.plot(prob)
print(prob)
numpy scipy standard-deviation probability-density
numpy scipy standard-deviation probability-density
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
edited Nov 24 '18 at 1:58
Edu Ariño Pelegrín
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
asked Nov 24 '18 at 1:24
Edu Ariño PelegrínEdu Ariño Pelegrín
13
13
add a comment |
add a comment |
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