I thought I understood all the theory quite well and sat down to begin coding until I realized that calculating a probability at a point within a normal distribution in the application of bayes' rule you can't simply plug the point into the normal distribution and get the value since the probability is a density. How do you approach this from a numerical standpoint or am I incorrect? My guess is that you need to leverage the cumulative distribution function instead and calculate the probability over some small delta around the point.(adsbygoogle = window.adsbygoogle || []).push({});

My broader problem is now after having gone through a good part of probability and statistics and being able to wield the P notation quite deftly on paper, I'm finding that translating those theories to actual computation has its own challenges and I'm wondering how this is generally approached or if I'm completely misunderstanding something. I'm fairly certain that P(x) doesn't mean evaluating the pdf f(x) and just taking that value.

Appreciate any guidance.

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# Calculating a probability given a point for a continuous distribution?

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