tag:blogger.com,1999:blog-32079676.post115498130278423000..comments2020-07-18T10:37:06.896-07:00Comments on RRResearch: Probability ≠ likelihoodRosie Redfieldhttp://www.blogger.com/profile/06807912674127645263noreply@blogger.comBlogger4125tag:blogger.com,1999:blog-32079676.post-2019375354778231062012-04-24T18:54:20.303-07:002012-04-24T18:54:20.303-07:00I think your definitions are the wrong way around....I think your definitions are the wrong way around. From wikipedia:<br />"In non-technical parlance, "likelihood" is usually a synonym for "probability" but in statistical usage, a clear technical distinction is made. One may ask "If I were to flip a fair coin 100 times, what is the probability of it landing heads-up every time?" or "Given that I have flipped a coin 100 times and it has landed heads-up 100 times, what is the likelihood that the coin is fair?" but it would be improper to switch "likelihood" and "probability" in the two sentences."<br /><br />Probability talks about the chances of the observation, likelihood refers to the chances of the parameters being correct given an observation.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-32079676.post-82295646462807450952011-04-02T05:04:30.972-07:002011-04-02T05:04:30.972-07:00Just to add, likelihood function need not define a...Just to add, likelihood function need not define a probability measure i.e. the integral need not add up to 1.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-32079676.post-46197205474126486792011-03-02T18:08:45.969-08:002011-03-02T18:08:45.969-08:00I think you could put your general definition even...I think you could put your general definition even ore simply: probability is the chance of an event/data given certain values of parameters and likelihood is the chance of certain values of parameters given the event/data.Anonymousnoreply@blogger.comtag:blogger.com,1999:blog-32079676.post-1155100892292183922006-08-08T22:21:00.000-07:002006-08-08T22:21:00.000-07:00"For now I'm not going to worry about why this mig..."For now I'm not going to worry about why this might matter."<BR/><BR/>It may matter even less than you think, at least in phylogenetics. MrBayes does use Bayesian statistics to infer phylogenetic relationships among taxa. However, most people give the program a "flat" prior, which basically makes it a Maximum Likelihood analysis. <BR/><BR/>So why even use MrBayes? Why not just use a ML method? ML analyses are notoriously computationally intensive and that is just to produce one tree and it is best to resample the data and produce a distribution of trees to calculate statistical support for branches (bootstrap). <BR/><BR/>However, bootstrapping is not needed for MrBayes because the program outputs the probability of each node. So basically, MrBayes saves time and produces similar results to ML. And you can use a lot of sophisticated evolutionary models in your analysis.<BR/><BR/>Sorry if this bores you......but now you have me thinking of Bayesian versus Maximum Likelihood and since I am publishing a paper using these methods, I keep asking.........what is the point?Legacy Userhttps://www.blogger.com/profile/02513738503992214008noreply@blogger.com