사후 확률

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위키데이터

• ID : Q278079

말뭉치

1. A posterior probability is the probability of assigning observations to groups given the data.
2. If you know or can estimate these probabilities, a discriminant analysis can use these prior probabilities in calculating the posterior probabilities.
3. …of all joint probabilities, the posterior probability is arrived at.
4. Posterior probability is the likelihood that the individual, whose genotype is uncertain, either carries the mutant gene or does not.
5. Posterior probability is a revised probability that takes into account new available information.
6. Given any report r submitted to the CPS server, MLA guarantees that the posterior probability of each query content in r is larger than 0.
7. There are many ways to summarize the posterior distribution of tree topologies and branch lengths.
8. The 50% majority consensus tree is a tree constructed so that it contains all of the clades that occur in at least 50% of the trees in the posterior distribution.
9. In other words it contains only the clades that have a posterior probability of >= 50%.
10. It has sometimes been used to describe the tree associated with the sampled state in the MCMC chain that has the highest posterior probability density.
11. Bayes factors are used as an intermediate step in calculating the posterior probabilities of each hypothesis.
12. Figure 3 shows a standard Bayesian updating of a prior distribution to a posterior distribution based on the data (likelihood).
13. We conclude that the posterior probability of H 0 provide a much more conservative quantification of the mode detection than the significance level.
14. A description of the network structure is given first, followed by an explanation of the posterior probability-updating algorithm.
15. Individuals were assigned to the group with the largest posterior probability estimate.
16. Prior to observing x, this distribution is the prior probability p(h); after observing x, it is the posterior probability p(h x).
17. At the same time, neither effect size, nor confidence interval estimate, nor posterior probability can be used to exclude chance as an explanation of data.
18. In Scott (2002) and Congdon (2006), a new method is advanced to compute posterior probabilities of models under consideration.
19. While it is indeed possible to approximate posterior probabilities based solely on MCMC outputs from single models, as demonstrated by Gelfand and Dey (1994) and Bartolucci et al.
20. Bayes' theorem can be used to estimate the posterior probabilities, that is, the probabilities that an email which is received as spam, really is spam, or is in fact legitimate.
21. In statistics, the posterior probability expresses how likely a hypothesis is given a particular set of data.
22. The posterior probability is one of the quantities involved in Bayes' rule.
23. This can be done by computing the quantiles of the posterior distribution.
24. Essentially this boils down to summarizing the posterior distribution by a single number.
25. When \(q\) is a continuous-valued variable, as here, the most common Bayesian point estimate is the mean (or expectation) of the posterior distribution, which is called the “posterior mean”.
26. In this contribution we show that the algorithm can also be used to estimate the posterior probability, or the confidence of its decision on each test instance.
27. For most problems encountered in fisheries stock assessment, it is impossible to evaluate the posterior distribution (Equation 1.5) analytically.
28. Let q denote the parameter vector and p( q ), the posterior probability of the parameter vector q .
29. q i : i=1,2,...} from the posterior distribution, p( q ), or to determine the relative posterior probabilities for a set of pre-specified vectors of parameters.
30. The posterior probability for each combination of the two parameters is then calculated using Equation (1.6).
31. Posterior probability is a conditional probability conditioned on randomly observed data.
32. In classification, posterior probabilities reflect the uncertainty of assessing an observation to particular class, see also Class membership probabilities.
33. While statistical classification methods by definition generate posterior probabilities, Machine Learners usually supply membership values which do not induce any probabilistic confidence.
34. A posterior probability, in Bayesian statistics, is the revised or updated probability of an event occurring after taking into consideration new information.
35. The posterior probability is calculated by updating the prior probability using Bayes' theorem.
36. Posterior probability distributions should be a better reflection of the underlying truth of a data generating process than the prior probability since the posterior included more information.
37. A posterior probability can subsequently become a prior for a new updated posterior probability as new information arises and is incorporated into the analysis.
38. Posterior probability is the probability an event will happen after all evidence or background information has been taken into account.
39. The posterior distribution is a way to summarize what we know about uncertain quantities in Bayesian analysis.
40. In other words, the posterior distribution summarizes what you know after the data has been observed.
41. P(Θ|data) on the left hand side is known as the posterior distribution.
42. We don’t care about the normalising constant so we have everything we need to calculate the unnormalised posterior distribution.
43. Now we have the posterior distribution for the length of a hydrogen bond we can derive statistics from it.
44. One of the most common statistics calculated from the posterior distribution is the mode.