The Bayesian Case for the Resurrection
What do the probabilities actually say when you run the numbers yourself?
Why it matters
Skeptics often object: "No amount of evidence can overcome the low prior for a miracle." Bayesian analysis lets us test that claim rigorously. By assigning probabilities for each piece of evidence on two hypotheses — Resurrection and No-Resurrection — and multiplying the resulting Bayes factors, we can see for ourselves whether the evidence is strong enough to overcome a low prior. The interactive calculator below lets you pick your own numbers.
The main case
Timothy and 's landmark chapter in The Blackwell Companion to Natural Theology applies Bayesian reasoning to the resurrection. For each minimal fact (empty tomb, group appearances, Paul's conversion, James's conversion, early creed, willingness to die), the likelihood on Resurrection vastly exceeds the likelihood on No-Resurrection. When these Bayes factors multiply, the cumulative factor reaches the order of 10^40 or higher — overwhelming even extraordinarily low priors. You do not need to accept their specific numbers; even with conservative values the conclusion is robust. The calculator on this page lets you test that claim yourself. Enter your prior, adjust each likelihood, and watch the posterior update in real time.
Argument map
Bayes' theorem describes how any rational agent should update beliefs on evidence.
Each minimal fact is much more expected on Resurrection than on No-Resurrection.
When Bayes factors multiply, they grow quickly.
The cumulative Bayes factor overwhelms any plausibly-low prior.
Given the evidence, a rational posterior probability for the resurrection is decisively high, even from a very low prior.
Miracles have "zero prior" so no evidence can raise the posterior.
A literal zero prior is pathological; it cannot be updated at all. Setting a very small but nonzero prior preserves Bayesian updating, and even tiny priors are overcome by large cumulative Bayes factors.
The likelihoods are subjective guesses.
They are constrained estimates. The calculator lets you dial them down aggressively and see that the posterior is robust to wide parameter variation.
Bayesian arguments prove too much (any religion can use them).
Other religions typically lack the cluster of public, early, multiply-attested, hostile-witness-corroborated data that the resurrection has.
Bayesian resurrection calculator
Adjust the dials yourself. Your prior is how probable you considered Jesus\' resurrection before looking at the evidence. For each piece of evidence, estimate how likely it would be if the resurrection happened and how likely it would be if it did not. The calculator applies Bayes\' theorem sequentially and shows how your belief should update. Follow the work of Timothy & Lydia McGrew (Blackwell Companion to Natural Theology).
Tip: even a prior of 1 in 10,000 yields a posterior > 99% when the combined Bayes factor exceeds roughly 106.
- Forensic DNA identification evidence in court typically runs at about 1 in 109 to 1012. A cumulative factor of 1040 is about a trillion trillion times stronger than the DNA match that convicts a defendant beyond reasonable doubt.
- Particle physicists declare a "discovery" at 5-sigma, about 1 in 3.5 million. 1040 is equivalent to roughly a 13-sigma result — the level at which physicists stop calling it evidence and start calling it the new baseline.
- It is the Bayesian weight of guessing a specific atom in a kilogram of matter, blindfolded, on the first try — and the evidence here points that strongly toward one hypothesis.
- Suppose you think resurrection is so unlikely the prior is 1 in a billion (10-9). A Bayes factor of 1040 still leaves you with posterior odds of 1031 to 1 in favor.
- To defeat this evidence with priors alone you would need to be more certain miracles are impossible than any human being is ever justified in being about anything — more certain than you are that the sun will rise, that other minds exist, or that you are not dreaming.
- Hume's "no testimony is sufficient" argument is often quoted here — but Hume was arguing before Bayes' theorem was widely understood. Applied rigorously, his standard quietly demands a prior so extreme it is unreachable.
If you would convict a stranger of murder on a 1012 DNA match, consistency requires you to take a 1040 cumulative case seriously. Either the ordinary standards of evidence apply here too — or you are making an exception precisely because of what the evidence points to.
Put the Bayesian calculator on your site
Let your readers adjust the same dials themselves. The embed is a standalone, responsive iframe.
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allow="clipboard-write"
></iframe>Claim · Evidence · Objection · Response
1.Bayes' theorem is the correct framework for evaluating historical claims.
Majority viewEvidence
- Bayes' theorem follows uniquely from the axioms of probability.
- Scholarly historians routinely reason about "expected / unexpected" evidence, which is Bayesian reasoning in prose.
- , , and atheist philosopher John Earman have applied Bayesian analysis to miracle claims.
Strongest objection
"Bayesian reasoning gives the illusion of precision with subjective numbers."
Response
Any reasoning with uncertain evidence depends on estimates. Bayesianism makes the dependencies explicit and lets us see which inputs would have to change to flip the conclusion.
- "The Argument from Miracles" (Blackwell Companion to Natural Theology) — Timothy & Lydia McGrew (2009)scholarlyFind on Amazon
- Reasonable Faith — William Lane Craig (2008 (3rd ed.))scholarlyFind on Amazon
2.Each piece of resurrection evidence yields a large Bayes factor.
DebatedEvidence
- Empty tomb: P(empty | R) is near 1; P(empty | ~R) is low.
- Group appearances: group hallucinations are clinically unattested on ~R.
- Paul: P(Paul converts | R) is high; P(Paul converts | ~R) is very low — he was actively hostile.
- James: no grief motive; had already dismissed his brother.
- Early creed: legend needs time.
Strongest objection
"You are stacking the deck with favorable likelihoods."
Response
The calculator gives you the dials. Drop P(evidence | R) to 0.5 and raise P(evidence | ~R) to 0.3 across the board and the cumulative Bayes factor still outruns a 1-in-10,000 prior.
- "The Argument from Miracles" (Blackwell Companion to Natural Theology) — Timothy & Lydia McGrew (2009)scholarlyFind on Amazon
- The Resurrection of Jesus: A New Historiographical Approach — Michael Licona (2010)scholarlyFind on Amazon
- The Case for the Resurrection of Jesus — Gary Habermas & Michael Licona (2004)scholarlyFind on Amazon
3.The cumulative factor overwhelms any plausibly low prior.
DebatedEvidence
- & estimate a cumulative Bayes factor near 10^44 for the core resurrection data.
- Even reducing this by 30 orders of magnitude (to 10^14) still overwhelms a 1-in-10-billion prior.
- Posterior odds = prior odds × cumulative Bayes factor.
- For any finite prior, a sufficiently large Bayes factor yields a posterior arbitrarily close to 1.
Strongest objection
"A posterior close to 1 is implausible for any historical claim."
Response
Bayesianism tracks what your beliefs should be given the evidence. If many independent lines all point the same direction, a high posterior is the rational response.
- "The Argument from Miracles" (Blackwell Companion to Natural Theology) — Timothy & Lydia McGrew (2009)scholarlyFind on Amazon
What scholars debate
Bayesian treatments of the resurrection are controversial in NT studies because historians often prefer narrative over formal probability. The ' chapter in & 's Blackwell Companion (2009) is the technical reference. Critics argue the priors should be effectively zero; responders note a literal zero prior is inconsistent with Bayesian updating.
Reflection
- 1.What prior would you honestly start with, and why?
- 2.Which of the evidence likelihoods in the calculator would you push back on? By how much?
- 3.At what combined Bayes factor would you consider the resurrection rationally warranted, even from your chosen prior?
Key sources
- "The Argument from Miracles" (Blackwell Companion to Natural Theology) — Timothy & Lydia McGrew (2009)scholarlyFind on Amazon
- Reasonable Faith — William Lane Craig (2008 (3rd ed.))scholarlyFind on Amazon
- The Resurrection of Jesus: A New Historiographical Approach — Michael Licona (2010)scholarlyFind on Amazon
- The Case for the Resurrection of Jesus — Gary Habermas & Michael Licona (2004)scholarlyFind on Amazon
Featured thinkers
Leading resurrection scholar who developed the Minimal Facts approach, cataloging claims accepted by a broad majority of critical historians.
Historian specializing in the resurrection, ancient biography, and Greco-Roman historiography.
One of the most prolific New Testament historians of his generation. His 800-page Resurrection of the Son of God situates the resurrection within Second Temple Jewish expectations and mounts a historical case that the bodily resurrection is the best explanation.
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