NaClhv

Theology, philosophy, math, science, and random other things
                                                                                                                                                                                                                                                                  

Bayesian evaluation for the likelihood of Christ's resurrection (Part 42)

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 […]
2017-01-23

Bayesian evaluation for the likelihood of Christ's resurrection (Part 41)

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 […]

Bayesian evaluation for the likelihood of Christ's resurrection (Part 40)

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 […]

Bayesian evaluation for the likelihood of Christ's resurrection (Part 39)

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 […]

Bayesian evaluation for the likelihood of Christ's resurrection (Part 38)

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% […]
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