StackOverflow2013

Note that there are some explanatory texts on larger screens.

plurals

PO
primarykey
Id
7979093
data
AcceptedAnswerId
0
AnswerCount
0
ClosedDate
CommentCount
0
CommunityOwnedDate
CreationDate
2011-11-02T10:49:17.033
FavoriteCount
0
LastActivityDate
2011-11-02T10:49:17.033
LastEditDate
LastEditorUserId
0
OwnerUserId
1023815
ParentId
7978759
PostTypeId
2
Score
3
ViewCount
0
LastEditorDisplayName
text
Body
In general to generate random numbers from an arbitrary distribution you'd first generate uniform random numbers and then pass them to the inverse of the cumulative distribution function. Assume for example that you want random numbers with uniform distribution on the interval [-10.0, 10.0] and all you've got is random numbers from [0.0, 1.0]. Cumulative distribution function of the uniform distribution on [-10.0, 10.0] is: <pre><code>cdf(x) = 0.05 * x + 0.5 for x in [-10.0, 10.0] </code></pre> This expresses the probability that a random number generated is smaller than x. The inverse is <pre><code>icdf(y) = 20.0 * y - 10.0 for y in [0.0, 1.0] </code></pre> (You can obtain this easily on paper by switching the x and y axis). Hence to obtain random numbers uniformly distributed on [-10.0, 10.0] you can use the following code: <pre><code>#include <stdlib.h> // Returns uniformly distributed random numbers from [0.0, 1.0]. double uniform0to1Random() { double r = random(); return r / ((double)RAND_MAX + 1); } // Returns uniformly distributed random numbers from [-10.0, 10.0]. double myRandom() { return 20.0 * uniform0to1Random() - 10.0; } </code></pre> In fact, you don't need uniform0to1Random() since there are already a lot of good uniform random numbers generators from [0.0, 1.0] (e.g. in the boost library). You can use the method to generate random numbers with nearly any probability distribution you want by sampling the inverse cumulative distribution as shown above. See <a href="http://en.wikipedia.org/wiki/Inverse_transform_sampling" rel="nofollow">http://en.wikipedia.org/wiki/Inverse_transform_sampling</a> for more details.
Tags
Title
singulars
PostAcceptedAnswerId
1. This table or related slice is empty.
PostParentId
1. POGenerate Float Random Values (also negative)
 singulars
 PostTypePostTypeId
 PTQuestion
PostTypePostTypeId
1. PTAnswer
UserLastEditorUserId
1. This table or related slice is empty.
UserOwnerUserId
1. USAdam Zalcman
plurals
PostLinksPostIdRelatedPostId
1. This table or related slice is empty.
PostLinksRelatedPostIdPostId
1. This table or related slice is empty.
PostsAcceptedAnswerId
1. POGenerate Float Random Values (also negative)
 singulars
 PostTypePostTypeId
 PTQuestion
PostsParentIdCreationDate
1. This table or related slice is empty.
VotesPostIdCreationDate
1. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
2. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTAcceptedByOriginator
3. VO
 singulars
 PostPostId
 PO
 UserUserId
 This table or related slice is empty.
 VoteTypeVoteTypeId
 VTUpMod
CommentsPostId
1. This table or related slice is empty.

Querying!

Guidance

A row detail

Detail views are divided into sections. All the information in the data section comes from columns in the selected row. The other sections display data from other, related rows.

Related data can be related in a to-one or a to-many fashion. Captions of data related in a to-many fashion link to a list view showing a filtered view of the table.

Try moving around until you find a non-empty to-many entry and click on the label to get to one. You can move back to the root by clicking on the database name in the header.