I have long been fascinated by the idea of karma. The notion itself is just built on simple empiricism (that of causality) but once you accept a single metaphysical prior – that of rebirth and the quest for moksha from the cycle – it changes into a beautifully complex meme.

Now as a man is like this or like that,

according as he acts and according as he behaves, so will he be;

a man of good acts will become good, a man of bad acts, bad;

he becomes pure by pure deeds, bad by bad deeds;

And here they say that a person consists of desires,

and as is his desire, so is his will;

and as is his will, so is his deed;

and whatever deed he does, that he will reap.

Now let us take a not so straightforward approach to thinking of karma. If you have been reading me you would have gotten the feeling I am a Bayesian. If you haven’t, I am a Bayesian, which is no different (at least in my mind) from saying “I believe in karma”.

First a brief foray into Gaussian probability. This is a bell curve otherwise known as a Normal Distribution or a Gaussian distribution. It has this nice property that when you find the x value at the centre of it’s hump (which is called it’s **mean**) the y value that you get which is it’s probability P(x) is the highest, higher than any other point. The width of this curve is given by it’s **variance. **A higher variance means a really fat curve.

Now let there be an event A which non-trivially occurs in a significant number of people’s lives. Let’s take the life of someone in brahmacharya asrama. Now for said person, him getting married is a non-zero proability event i.e it will happen at some point in time. If you ignore the waffling stupidity of this chap in the Hindu you learn that the average age of marriage in urban india is about 29-30 for men. (When paternal age effect kicks in, in about 2 generations things are not going to be looking good TL;DR get married early, have kids early, thank me later.)

What that means is if we took age as our x axis you would see that the graph would have it’s mean at around 29 years. But this is the average which is, let’s face is not a very accurate measure of a population all the time. Outliers skew the average and have to be pruned.

Now let’s take one particular persons probability distribution for event A = marriage. Now here’s where things get a little tricky. Marriage in itself is not something that just occurs at random. There are factors influencing it, which is captured by the curves mean and variance. What do I mean by this? Well let’s say we have a chap Rohan with a gaussian curve which describes event A (marriage) but it is hidden from us. He comes from a very conservative family in Haryana who believe that he is best married off as soon as possible, to a nice comely girl at say age 23-25. Well then his gaussian curve has a mean at around 24 with high probability of marriage in the ages around 24, but the probability of him getting married at say age 30 is practically 0 and this is exactly what the gaussian curve captures. Beautiful isn’t it.

What influenced Rohan’s mean and variance? His family. What influenced Rohan being born into that family? Karma.

There are an infinite number of events that can occur in one’s life and therefore an infinite number of gaussian distributions. Life, and therefore karma is a multivariate gaussian distribution.

So where does Bayesianism come into all of this? Well let’s say when Rohan is 21 his mum catches him with a girl. After the initial shock, she starts finding a girl for him to marry. Now this latent curve of his marriage event is updated, it’s mean and variance changes. His variance becomes lower and his mean shifts towards the left of the graph.

You can trivially think of a converse example, say his parents have a bit of financial stress when he is age 23 and he is forced to work and forgo marriage.

While my illustration is a little silly, it captures the beauty of gaussian distributions and how you can model all life events as a gaussian curve that gets updated, as and when events occur in one’s life. But a consequence of accepting karma as a prior is that there are predetermined mean and variances for each distribution. Not only this; these distributions influence each other. One life event happening influences a subset of the multivariate distribution and updates it in a Bayesian fashion. These predetermined mean, variance tuples are simply measures of your past karma because of … you guessed it, Bayesianism.

Kismat is Bayeisan. Get to work.

An Unknown Man

said:I’d like to know your thoughts on this one.

http://www.advaita-vedanta.org/articles/The_Riddle_of_Fate_and_Free.htm

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pagantrad

said:Haha, wait for part 2 🙂

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Sahil Kakkar

said:I have never read so profound yet simple explanation to understanding of indic systems of thought. Specially the last lines are a charm :

“consequence of accepting karma as a prior is that there are predetermined mean and variances for each distribution. Not only this; these distributions influence each other. One life event happening influences a subset of the multivariate distribution and updates it in a Bayesian fashion. These predetermined mean, variance tuples are simply measures of your past karma because of … you guessed it, Bayesianism”

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