Well, first of all, you’re probably wondering what MCMC is, aren’t you?  Hmmm.  So I guess I’ll explain that first.  MCMC methods, or Markov Chain Monte Carlo methods, involve sampling from a posterior distribution.  And what the heck is a posterior distribution?  It’s a distribution derived from a prior distribution and something called the likelihood obtained from our data.  And what the heck is a prior distribution?  Are you still awake?  Are you still with me?  Just stay with me a bit longer and we’ll get to something cool.  I’m promise!  So a prior distribution gives you the 411 on any parameters you have based on any prior information you have.  But once you get more data, the way you see the parameters could change so your posterior distribution may change.  And that’s the basis of Bayesian inference, by the way.  Now, how the heck does any of this relate to my books or the multiverse theory in general?

Well, I remember having dinner with my friend, Lynn, once and she was saying that one of the problems she had with the multiverse theory is that we simply cannot exist in all dimensions all the time.  And you know what?  She’s right!  How’s that, you say?  Well, I’m gonna tell ya right now, you say. Well, I’ll tell you right now, I say.  Great, you say.  Okay then, I say.  So let’s say Person A gets together with Person B in one dimension and they have a Kid Z.  Why Z, you ask?  Well, I’m getting to that too, I say.  Okay, you say.  Now, let’s say Person A doesn’t get together in Person B in another dimension but with Person C.  Because we already know that A and B are Kid Z’s parents, we can deduce that he or she could not exist if A got together with C instead.  So A and C could have Kid X or Kid Y together, but not Kid Z.  In this instance, the parents make up the prior information and the probability of what kid will be born in a certain dimension could then be determined from the updated posterior distribution of information.  Like in my books, there is this character, Anton Zelov.  By the way, remember that name.  You’ll hear it all over the place in a few years.  And remember the guy who will play the role of Anton Zelov.  I believe Robert Pattinson might send him a fruit basket with a note saying, “God bless you, man!  They’re your problem now!”  No, I shouldn’t say that.  That’s sorta mean to fangirls.  Might be true, but mean.  Personally, I would love all my fangirls … if I ever get any, that is.  I’m a fangirl myself of Brian Greene, Lisa Randall, and Michio Kaku.  And of my husband, Joe Manganiello, of course.  Husband?  What?  Wait … was that last one aloud?  Um … anyway, there’s some dimensions where Anton’s together with one chick and they have a son.  And there are other dimensions where’s with another woman with whom he has one or two daughters, one or two depending on the realm they’re in.  And there are dimensions where he is married to the protagonist (of the first book, anyway), Jane Kremowski, but they have no children together.  So it’s sorta like an inter-dimensional Maury Povich episode.  Now, once we have our posterior distribution, we could randomly draw values according to the probabilities associated with that distribution.  But how about we cover randomness next time.  Now, if only I could randomly enter that dimension situated in Bermuda.  Did I mention how rough this Chicago winter has been?  Ah well — I guess I’ll just have to settle for this image now.

berm

 

Until next time — wishing you margarita wishes and pina colada dreams!

 

 

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