| |
 |
|
|
|
| |
 |
|
| |
|
|
| |
| |
Non-Informative Prior
A prior distribution for a parameter vector which is intended
to express ignorance about This can be tricky, and often leads
to the use of densities which have an infinite integral (known
as improper priors), such as a uniform density or |
|
|
|
| |
|
|
| |
| |
Normal Distribution
In our usage, this includes both univariate and multivariate
distributions. The density is |
|
|
|
| |
|
|
| |
| |
NP-Complete
It is desirable that algorithms terminate in a time which is
polynomial in the size of the problem and the accuracy required.
Let P denote all problems for which there is such an algorithm.
If we allow nondeterministic algorithms (which are allowed to
choose the best option whenever there is a choice) the class
of problems is called NP. Equivalently, NP is the class of problems
for which a solution could be verified in polynomial time. It
is widely believed that NP is strictly larger than P, but this
remains an open research problem. A problem in NP is called
NP-complete if proving if proving it was in P would establish
P = NP, which should be regarded as strong evidence that no
polynomial algorithm will ever be found. (Cormen et al., 1990;
Sedgewick, 1990; Garey & Johnson, 1979.) |
|
|
|
| |
|
|
| |
| |
NP-Hard
A NP-hard problem is one that implies a solution to every problem
in NP (see NP-complete) but is not known to be in NP. Thus NP-hardness
is strong evidence that no polynomial algorithm for the problem
will ever be found. |
|
|
|
| |
|
|
| |
|
|
|
|
