Skip to content
Projects
Groups
Snippets
Help
Loading...
Help
Support
Submit feedback
Contribute to GitLab
Sign in
Toggle navigation
bob.measure
Project
Project
Details
Activity
Releases
Cycle Analytics
Repository
Repository
Files
Commits
Branches
Tags
Contributors
Graph
Compare
Charts
Issues
0
Issues
0
List
Boards
Labels
Milestones
Merge Requests
0
Merge Requests
0
CI / CD
CI / CD
Pipelines
Jobs
Schedules
Charts
Members
Members
Collapse sidebar
Close sidebar
Activity
Graph
Charts
Create a new issue
Jobs
Commits
Issue Boards
Open sidebar
bob
bob.measure
Commits
3798c61e
Commit
3798c61e
authored
Mar 22, 2018
by
Theophile GENTILHOMME
Browse files
Options
Browse Files
Download
Email Patches
Plain Diff
Math support for conf inter doc
parent
3a88f2c7
Pipeline
#17794
passed with stage
in 19 minutes and 52 seconds
Changes
1
Pipelines
1
Hide whitespace changes
Inline
Side-by-side
Showing
1 changed file
with
11 additions
and
7 deletions
+11
-7
doc/guide.rst
doc/guide.rst
+11
-7
No files found.
doc/guide.rst
View file @
3798c61e
...
...
@@ -208,18 +208,22 @@ Both functions require that at least one probe item exists, which has no accordi
Confidence interval
-------------------
A confidence interval for parameter `x` consists of a lower
estimate `L`, and an upper estimate `U`, such that the probability of the true value being
within the interval estimate is equal to `\alpha`. For example,
a 95% confidence interval (i.e. `\alpha = 0.95`) for a parameter `x` is given by `[L, U]` such that
`Prob(x∈[L,U]) = 95%`. The smaller the test size, the wider the confidence
interval will be, and the greater `alpha`, the smaller the confidence interval
A confidence interval for parameter :math:`x` consists of a lower
estimate :math:`L`, and an upper estimate :math:`U`, such that the probability
of the true value being within the interval estimate is equal to :math:`\alpha`.
For example, a 95% confidence interval (i.e. :math:`\alpha = 0.95`) for a
parameter :math:`x` is given by :math:`[L, U]` such that
.. math:: Prob(x∈[L,U]) = 95%
The smaller the test size, the wider the confidence
interval will be, and the greater :math:`\alpha`, the smaller the confidence interval
will be.
`The Clopper-Pearson interval`_, a common method for calculating
confidence intervals, is function of the number of success, the number of trials
and confidence
value `\alpha` is used as :py:func:`bob.measure.utils.confidence_for_indicator_variable`.
value
:math:
`\alpha` is used as :py:func:`bob.measure.utils.confidence_for_indicator_variable`.
It is based on the cumulative probabilities of the binomial distribution. This
method is quite conservative, meaning that the true coverage rate of a 95%
Clopper–Pearson interval may be well above 95%.
...
...
Write
Preview
Markdown
is supported
0%
Try again
or
attach a new file
Attach a file
Cancel
You are about to add
0
people
to the discussion. Proceed with caution.
Finish editing this message first!
Cancel
Please
register
or
sign in
to comment