Python Equivelent of R BetaExpert function
Do any of the Python libraries provide an equivelent of the BetaExpert function from R? (https://rdrr.io/cran/prevalence/man/betaExpert.html)
What this does is lets you give expert opinion (ie 90% sure it's more than 0.7, most likely 0.9) and outputs the alpha/beta parameters for a Beta distribution that covers this.
scipy.stats.beta and numpy.random.beta have a various functions but nothing comparable to the R function above that I can find.
Thanks,
GD
python numpy scipy
asked Nov 10 '18 at 18:26
user10633897
1
add a comment |
Do any of the Python libraries provide an equivelent of the BetaExpert function from R? (https://rdrr.io/cran/prevalence/man/betaExpert.html)
What this does is lets you give expert opinion (ie 90% sure it's more than 0.7, most likely 0.9) and outputs the alpha/beta parameters for a Beta distribution that covers this.
scipy.stats.beta and numpy.random.beta have a various functions but nothing comparable to the R function above that I can find.
Thanks,
GD
python numpy scipy
asked Nov 10 '18 at 18:26
user10633897
1
add a comment |
Do any of the Python libraries provide an equivelent of the BetaExpert function from R? (https://rdrr.io/cran/prevalence/man/betaExpert.html)
What this does is lets you give expert opinion (ie 90% sure it's more than 0.7, most likely 0.9) and outputs the alpha/beta parameters for a Beta distribution that covers this.
scipy.stats.beta and numpy.random.beta have a various functions but nothing comparable to the R function above that I can find.
Thanks,
GD
python numpy scipy
asked Nov 10 '18 at 18:26
user10633897
1
Do any of the Python libraries provide an equivelent of the BetaExpert function from R? (https://rdrr.io/cran/prevalence/man/betaExpert.html)
What this does is lets you give expert opinion (ie 90% sure it's more than 0.7, most likely 0.9) and outputs the alpha/beta parameters for a Beta distribution that covers this.
scipy.stats.beta and numpy.random.beta have a various functions but nothing comparable to the R function above that I can find.
Thanks,
GD
python numpy scipy
python numpy scipy
asked Nov 10 '18 at 18:26
user10633897
1
asked Nov 10 '18 at 18:26
user10633897
1
asked Nov 10 '18 at 18:26
user10633897
1
asked Nov 10 '18 at 18:26
user10633897
1
asked Nov 10 '18 at 18:26
user10633897
1
1
add a comment |
add a comment |
1 Answer
1
active
oldest
votes
The solution to this is given at https://www.codeproject.com/Articles/56371/Finding-Probability-Distribution-Parameters-from-P
from scipy import stats, optimize
def beta_parameters(x1, p1, x2, p2):
"Find parameters for a beta random variable X so that P(X > x1) = p1 and P(X > x2) = p2."
def square(x):
return x*x
def objective(v):
(a, b) = v
temp = square( stats.beta.cdf(x1, a, b) - p1 )
temp += square( stats.beta.cdf(x2, a, b) - p2 )
return temp
# arbitrary initial guess of (3, 3) for parameters
xopt = optimize.fmin(objective, (3, 3))
return (xopt[0], xopt[1])
answered Nov 11 '18 at 22:21
user10633897
1
add a comment |
Your Answer
StackExchange.ifUsing("editor", function ()
StackExchange.using("externalEditor", function ()
StackExchange.using("snippets", function ()
StackExchange.snippets.init();
);
);
, "code-snippets");
StackExchange.ready(function()
var channelOptions =
tags: "".split(" "),
id: "1"
;
initTagRenderer("".split(" "), "".split(" "), channelOptions);
StackExchange.using("externalEditor", function()
// Have to fire editor after snippets, if snippets enabled
if (StackExchange.settings.snippets.snippetsEnabled)
StackExchange.using("snippets", function()
createEditor();
);
else
createEditor();
);
function createEditor()
StackExchange.prepareEditor(
heartbeatType: 'answer',
autoActivateHeartbeat: false,
convertImagesToLinks: true,
noModals: true,
showLowRepImageUploadWarning: true,
reputationToPostImages: 10,
bindNavPrevention: true,
postfix: "",
imageUploader:
brandingHtml: "Powered by u003ca class="icon-imgur-white" href="https://imgur.com/"u003eu003c/au003e",
contentPolicyHtml: "User contributions licensed under u003ca href="https://creativecommons.org/licenses/by-sa/3.0/"u003ecc by-sa 3.0 with attribution requiredu003c/au003e u003ca href="https://stackoverflow.com/legal/content-policy"u003e(content policy)u003c/au003e",
allowUrls: true
,
onDemand: true,
discardSelector: ".discard-answer"
,immediatelyShowMarkdownHelp:true
);
);
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53242101%2fpython-equivelent-of-r-betaexpert-function%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
1 Answer
1
active
oldest
votes
1 Answer
1
active
oldest
votes
active
oldest
votes
active
oldest
votes
The solution to this is given at https://www.codeproject.com/Articles/56371/Finding-Probability-Distribution-Parameters-from-P
from scipy import stats, optimize
def beta_parameters(x1, p1, x2, p2):
"Find parameters for a beta random variable X so that P(X > x1) = p1 and P(X > x2) = p2."
def square(x):
return x*x
def objective(v):
(a, b) = v
temp = square( stats.beta.cdf(x1, a, b) - p1 )
temp += square( stats.beta.cdf(x2, a, b) - p2 )
return temp
# arbitrary initial guess of (3, 3) for parameters
xopt = optimize.fmin(objective, (3, 3))
return (xopt[0], xopt[1])
answered Nov 11 '18 at 22:21
user10633897
1
add a comment |
The solution to this is given at https://www.codeproject.com/Articles/56371/Finding-Probability-Distribution-Parameters-from-P
from scipy import stats, optimize
def beta_parameters(x1, p1, x2, p2):
"Find parameters for a beta random variable X so that P(X > x1) = p1 and P(X > x2) = p2."
def square(x):
return x*x
def objective(v):
(a, b) = v
temp = square( stats.beta.cdf(x1, a, b) - p1 )
temp += square( stats.beta.cdf(x2, a, b) - p2 )
return temp
# arbitrary initial guess of (3, 3) for parameters
xopt = optimize.fmin(objective, (3, 3))
return (xopt[0], xopt[1])
answered Nov 11 '18 at 22:21
user10633897
1
add a comment |
The solution to this is given at https://www.codeproject.com/Articles/56371/Finding-Probability-Distribution-Parameters-from-P
from scipy import stats, optimize
def beta_parameters(x1, p1, x2, p2):
"Find parameters for a beta random variable X so that P(X > x1) = p1 and P(X > x2) = p2."
def square(x):
return x*x
def objective(v):
(a, b) = v
temp = square( stats.beta.cdf(x1, a, b) - p1 )
temp += square( stats.beta.cdf(x2, a, b) - p2 )
return temp
# arbitrary initial guess of (3, 3) for parameters
xopt = optimize.fmin(objective, (3, 3))
return (xopt[0], xopt[1])
answered Nov 11 '18 at 22:21
user10633897
1
The solution to this is given at https://www.codeproject.com/Articles/56371/Finding-Probability-Distribution-Parameters-from-P
from scipy import stats, optimize
def beta_parameters(x1, p1, x2, p2):
"Find parameters for a beta random variable X so that P(X > x1) = p1 and P(X > x2) = p2."
def square(x):
return x*x
def objective(v):
(a, b) = v
temp = square( stats.beta.cdf(x1, a, b) - p1 )
temp += square( stats.beta.cdf(x2, a, b) - p2 )
return temp
# arbitrary initial guess of (3, 3) for parameters
xopt = optimize.fmin(objective, (3, 3))
return (xopt[0], xopt[1])
answered Nov 11 '18 at 22:21
user10633897
1
answered Nov 11 '18 at 22:21
user10633897
1
answered Nov 11 '18 at 22:21
user10633897
1
answered Nov 11 '18 at 22:21
user10633897
1
1
add a comment |
add a comment |
Thanks for contributing an answer to Stack Overflow!
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Some of your past answers have not been well-received, and you're in danger of being blocked from answering.
Please pay close attention to the following guidance:
- Please be sure to answer the question. Provide details and share your research!
But avoid …
- Asking for help, clarification, or responding to other answers.
- Making statements based on opinion; back them up with references or personal experience.
To learn more, see our tips on writing great answers.
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
StackExchange.ready(
function ()
StackExchange.openid.initPostLogin('.new-post-login', 'https%3a%2f%2fstackoverflow.com%2fquestions%2f53242101%2fpython-equivelent-of-r-betaexpert-function%23new-answer', 'question_page');
);
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Sign up or log in
StackExchange.ready(function ()
StackExchange.helpers.onClickDraftSave('#login-link');
);
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Sign up using Google
Sign up using Facebook
Sign up using Email and Password
Post as a guest
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
Required, but never shown
