Wow, it's Christmas again! I wish everyone has a safe and blessed holiday season. Read this article from the New University about UCI students who are showing the Christmas spirit by engaging in various projects, including Operation Christmas Child.

Last year, I recorded some Christmas guitar tunes for family. You can also enjoy some of these amateur recordings: SilentNight.mp3, HarkTheHerald.mp3 (copyright 2004).

As I type, I'm hearing a song by Steven Curtis Chapman called "Christmas Is All In The Heart," which accurately sums up, in my opinion, what this holiday season should be about. Here are some lyrics:

Christmas is all in the heart,
That’s where the feeling starts,
And like a fire inside,
It touches every part,
‘Cause Christmas is all in the heart.

And even if no white snow falls,
That’s all right because
The joy can still be found,
Wherever you are,
‘Cause Christmas is all in the heart.

No, it’s not in the snow that may or may not fall,
And it’s not in the gifts around the tree;
It’s in the love heaven gave, the night our Savior came,
And that same love can still be found wherever you are,
‘Cause Christmas is all in the heart - It’s all in the heart.

How does a person internally choose whether to be in the Design camp or in the Darwinian camp? In order to account for the factors that would lead one to make a decision on the origins issue, I think that it is sensible to view this decision-making process in a Bayesian framework. After all, a person's brain is a complex neural network that can be approximated in a probabilistic manner. Here is one application of Bayes rule:

P (theta | data) = P (data | theta) * P (theta) / P (data)

where

data = observations in nature,
theta = tweakable parameters of an underlying model which is implicitly assumed (but can be made explicit by adding an "M" random variable to the equation above).

To understand the equation above, consider the following background information:

• P (theta) is called the "prior" -- it is the prior probability of the theta parameters. It can be our prior belief of what those parameters should be.
• P (data | theta) is called the "likelihood" -- it is the likelihood of the data given a set of theta parameters.
• P (theta | data) is called the "posterior" -- it is the updated probability of the parameters in light of the data. In essence, we update our beliefs after seeing some data.
• P (data) is sometimes called "evidence" -- we can ignore it since it is just a normalization factor.

So we can rewrite our equation above ("~" means " approximates"):

Posterior ~ Likelihood * Prior

Now you may ask, "What does this have to do with one's views on the origins of life?" Stay tuned for the next post, since I am still in the process of formulating my ideas.