Did Jeffrey Epstein die by murder or suicide? In this episode, I argue that we should use Bayesian statistics to frame the debate. Indeed, we should use this approach to frame most "conspiracy theories". Most such theories are derided as compelling storytellers weaving half truths to fit their narrative.

Bayes offers a more analytical approach.

1. Make an educated guess about the probability of an event occurring. The likelihood of Epstein dying by suicide.

2. Identify authenticated clues that support that hypothesis.

3. Assess the odds of each clue happening independently, i.e. jail cameras not working, hyoid bone being broken, both guards falling asleep.

4. Then calculate odds of those happening together.

5. Perform calculation and then update original probability estimate based upon the probability those clues happening.

Using Bayes with a "little" help from Grok, I identify the odds of murder versus suicide.

I also identify ways that you should attack this analysis and not just my use of Grok.

This approach should be used more frequently as we try to resolve debates surrounding "conspiracies". I don't even really like that word. We're really trying to assess whether Event x was caused by y or z.