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 […]

Next, consider the factor of 24 that we used, as the ratio between the level of evidence for Jesus's resurrection, and that of the runner-up. This, too, was a very conservative estimate, which favors the skeptic's case. You'll recall that the runners-up were Aristeas and Krishna, with Apollonius falling not too far behind. In previously […]

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 […]

In the previous post, we demonstrated that the likelihood for Christ's resurrection came down to the number of "outliers" we can find in world history - where "outliers" are the other, non-Christian "resurrection" reports with at least a "some people say..." level of evidence behind them. The more such low-evidence cases we find, the more […]

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 […]

We've decided on a power law as the general form of the "skeptic's distribution". The details of the distribution near zero will not particularly matter. We're more concerned about how rapidly it decays at very large values. This allows us quite a bit of leeway in choosing the specific form of the power law distribution, […]

Recall that we're constructing a "skeptic's distribution" - the probability distribution of generating a resurrection report with a certain level of evidence. We will construct it from historical, empirical data. This allows us to bypass the mess of trying to compute everything from first principles, and ensures that the this is the correct distribution - […]

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