r333

The following url has the following conclusion

http://members.aol.com/SHinrichs9/jesustomb.pdf

Bayes equation, the basic formula commonly used to evaluate conditional probabilities, is the correct equation for evaluating the probabilities associated with the level of matching found between the Talpiot Tomb and the Gospel Jesus Family. Section 4.5.1 proves that Bayes equation is the correct formula for evaluating the probability for this type of problem. Thus, any formulation that is not consistent with Bayes equation would be invalid. Section 4.5.1.7 shows that the Feuerverger formulation is incorrect for the many reasons listed in Section 8.1. Other methods are also shown incorrect in Section 4.5.1.3.

Section 5 lists the equations that are used in Bayes equation to determine the extont and nonextont probability for the level of matching found between the Talpiot Tomb and the Gospel Jesus Family. The extont is fundamentally based on the number of other Jesus son of Josephs that could have been placed in the Talpiot Tomb. The nonextont is fundamentally based on the number of found inscribed male names on ossuaries that aside from their specific name would be consider as candidate ossuaries for the Gospel Jesus just as the Talpiot Tomb ossuaries are by TLTJ. Based on the calculations in Section 7 conservative odds of at least 59:1 against the Talpiot Tomb containing the Gospel Jesus are determined. The nonextont probability cannot be used to directly measure argument strength; however, it’s value of at least 0.669 is very high and is order of magnitudes higher than a minimum standard used in science use to make a significant argument. The probabilities imply that the match found is at a level well expected to occur just by random. Thus, valid probabilistic analysis of TLTJ clearly does not imply the Gospel Jesus was buried in the Talpiot Tomb.

Chris Weimer

Who is this Steve Hinrichs guy? What are his credentials? His homepage appears to be an apologetics page.

r333

I got a mastersdegree in engineering and work in a field where I do these type of probability calculations sometimes, but my credentials really do not matter, I explain the reasoning in the PDF. In fact I mathematically prove that bayes equation correctly solves problems of this type in Section 4.5.1.7. Don't worry about my credentials. Read the PDF and check it out for yourself.

Chris Weimer

"Don't worry about credentials." Sorry bub, maybe you can fool some people without credentials, but I'm not qualified to judge the soundness of statistical calculations. If you want the higher echelon to even bother giving you the time of day, then you had better pony up some valid credentials, or else remain with the dilettantes and the apologists.

Huon

from wiki :
The Talpiot Tomb (or Talpiyot Tomb) is a tomb discovered in 1980 in the East Talpiot neighbourhood five kilometers south of the Old City of Jerusalem, Israel. It contained ten ossuaries, six of them with epigraphs, including one with the inscription that has been interpreted as "Jesus, son of Joseph", though this text is disputed. The tomb also yielded various human remains and several carvings.

Chris Weimer

http://iidb.org/vbb/showthread.php?t=200403

http://iidb.org/vbb/showthread.php?t=198920

http://iidb.org/vbb/showthread.php?t=197833

In decreasing order of age.

Huon

An event A is the cause of an event B. Other events can also be the cause of event B. Bayes' theorem combines the probabilities of the events A and B.

Example 1 :

A very rare disease (event A) hits 1 person out of 100,000 (probability of event A).
There is a test (event B) for this disease. It is 99 % reliable (probability of event B), exact for 99 sick persons and false for 1 person, who is not sick, but declared positive (false positive). The persons who are not sick are always found negative (oversimplification, this is only an example).
Now, John Doe is tested. The test says "John Doe is positive".

Discussion : if the test is executed on 100,000 persons, we shall have 1 % error, that is, 1,000 persons will be declared positive. And really, 1 of these 1,000 will really be sick, and 999 will be in good health (false positive).

Example 2 :

A very rare property (event A) belongs to 1 person out of millions. He is the son of God, and God himself.
There is a test (event B) for this situation. It is 99 % reliable. It consists of looking at all the tombs who mention a Jesus ben Joseph, in or around Jerusalem, and rule out all the tombs which are too old, or too recent.

One tomb has been found positive. Is it a true positive, or is it a false positive ? What says Bayes ?

r333

Well, what I meant to say is there is no need to rely upon my credentials becuase the reasoning is fully explained in a way that can be fully critiqued. Even if you are not familiar with the subject I explain it all from the basics with examples.

Minimalist

Simcha Jacobovici had a statistician swear to his finds, too. "Lies, damn lies, and statistics" as the saying goes.

In any case, since it is exceedingly unlikely that there was ever a historical jesus to return from the dead ( I wonder what the statistical odds against that might be?) it is pretty clear that he wasn't buried in Talpiot or anywhere else.

Toto

Mark Goodacre links to a paper on the statistics that has academic credentials, but appears to be written for a lay audience.

Probability, Statistics, and the Talpiot Tomb by Kevin T. Kilty, Ph.D., P.E., Mark Elliott, Ph.D.

The blog also has some discussion and links from 2005, which this first became an issue