lukego
Joined 18 years ago
Joined 18 years ago
I hope that's preserved and one day published as e.g. the old MIT AI lab file system snapshots were.
(Robert Virding or Bjärne Däcker might well have a copy of the Prolog code to share if asked nicely.)
If so then estimating the marginal likelihood of each one and comparing them seems pretty reasonable?
(I mean in the sense of Jaynes chapter 20.)
Metcal have a great big doc on tip care that covers this.
I like to "wiggle" the chip with tweezers (under hot air) to see surface tension in action and judge whether the balls have all melted, but a tiny bit too much wiggle causes that problem.
I need to be careful that it doesn't interfere with my responsibility to raise independent kids who don't want or need money from me. This takes time and energy to make sure they can make the most of the social infrastructure available to them e.g. their school.
I'm very restrained in how I spend money for private consumption out of concern for setting an example that's unproductive e.g. making the kids feel pressure to earn a lot of money to sustain a lifestyle of luxury.
The center of a normal distribution has high likelihood (e.g. 1000000) if the standard deviation is small or low likelihood if the standard deviation is large (e.g. 1/1000000.)
This effect is amplified when you are working with products of likelihoods. They can be infinitesimal or astronomical.
Giant likelihoods really surprised me the first time I experienced them but they’re not uncommon when you work with synthetic test data in high dimensions and/or small scales.
They still integrate to the same magnitude because the higher likelihood values are spread over shorter spans.