Keyword | CPC | PCC | Volume | Score | Length of keyword |
---|---|---|---|---|---|
maximum a posteriori map hypothesis | 1.46 | 1 | 2525 | 73 | 35 |
maximum | 1.93 | 0.3 | 5699 | 8 | 7 |
a | 0.04 | 0.6 | 9266 | 79 | 1 |
posteriori | 1.38 | 0.6 | 477 | 79 | 10 |
map | 1.26 | 0.3 | 4671 | 73 | 3 |
hypothesis | 1.45 | 0.6 | 8424 | 38 | 10 |
Keyword | CPC | PCC | Volume | Score |
---|---|---|---|---|
maximum a posteriori map hypothesis | 1.16 | 0.6 | 8414 | 86 |
maximum a posteriori map method | 0.75 | 0.1 | 6071 | 70 |
maximum a posteriori map | 0.13 | 0.6 | 1782 | 5 |
maximum a posteriori map estimation | 1.53 | 0.3 | 1815 | 22 |
maximum likelihood vs maximum a posteriori | 1.33 | 0.6 | 7714 | 69 |
maximum a posteriori map estimator | 1.75 | 0.3 | 8236 | 37 |
the maximum a posteriori probability | 0.87 | 0.1 | 2047 | 93 |
maximum a posteriori example | 0.23 | 0.5 | 779 | 99 |
maximum a posteriori probability estimation | 1.01 | 1 | 6988 | 15 |
maximum a posteriori estimation | 1.52 | 0.5 | 5198 | 6 |
maximum a posteriori model complexity | 0.51 | 0.7 | 5578 | 3 |
maximum a posteriori estimate | 1.29 | 0.5 | 6150 | 2 |
maximum a posteriori example youtube | 0.69 | 0.4 | 1618 | 3 |
maximum a posteriori criterion | 1.52 | 1 | 7510 | 26 |
maximum a posteriori bayesian | 1.84 | 0.4 | 3224 | 45 |
finding a maximally specific hypothesis | 0.93 | 1 | 6729 | 2 |