When faced with the argument presented in Bayesian evaluation for the likelihood of Christ's resurrection, a common tactic is to try to push the prior lower. After all, this is one of the very few options available when one has no evidence on their side. But as I argued in the post itself, this will not […]

This was the state of the "Bayesian evaluation for the likelihood of Christ's resurrection" post, as of Easter 2018, in the "second draft" form. Some of the formatting has been lost in the blog migration, particularly in the Jupyter notebooks, but the content has been retained. This post will remain unchanged, while the other post […]

This is still a work in progress. It will change as I continue to add and edit the content. I consider this to be in its "third draft" form. It will take some more time to complete, and it may be messy in the meantime. A version of this post as it appeared on Easter […]

At long last, we can summarize this entire series. First, we calculated the prior odds for the resurrection of Jesus Christ. This prior cannot be zero. That would violate one of the fundamental tenets of Bayesian thinking, and it is not empirically justified, since we have not observed an infinite number of people who did […]

Let us summarize the "skeptic's distribution" argument for Christ's resurrection. We have already seen that any kind of reasonable investigation into Jesus's resurrection accounts would conclusively demonstrate that Jesus did rise from the dead. The only possibility left for the skeptic is to turn to unreasonable hypotheses - that is, to crackpot theories like conspiracies. […]

This is another Jupyter notebook. It contains python code that generates the probabilities of a "skeptic's distribution" generating a Jesus-level resurrection report. First, we import some modules: In [1]: import numpy as np import pandas as pd from scipy.stats import lognorm, genpareto We then write a function to simulate getting the maximum value out of n […]

We have established that the resurrection has, at a minimum, even odds of having taken place. Let us retrace our steps and demonstrate that this is, in fact, the minimum. Looking back, we see that our first decision was to choose a power law distribution as the "skeptic's distribution". As we mentioned when we made […]

This is a jupyter notebook. It contains the python code which generates the relationship between the number of "outliers" (as previously defined) and the probability of naturalistically generating a Jesus-level resurrection report. resurrection_calculation First, we import some modules: In [1]: %matplotlib inline import numpy as np import pandas as pd from scipy.stats import genpareto Next, we […]

So then, here is the summary of the basic idea: We assume that the "skeptic's distribution" will take the form of a generalized Pareto distribution. We will determine the shape parameter of the distribution by looking at how many "outliers" it has. A person's resurrection report is considered an "outlier" if it has at least 20% […]

Now, what kind of data do we have to determine the shape parameter? We have the historical data, of course. We have some number of people who are said to have been resurrected in some sense, and each of these people has some amount of evidence associated with their resurrection claim. We essentially want to […]

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