a prior probability造句

• As you say, having a prior probability distribution is key.
• The language acts as a prior probability.
• A particularly important application of Dirichlet processes is as a prior probability distribution in infinite mixture models.
• Probabilities before an inference are known as prior probabilities, and probabilities after are known as posterior probabilities.
• This requires that either the signal statistics is known or a prior probability for the signal can be specified.
• However, this does not mean the substring's absence is " impossible ", despite the absence having a prior probability of 0.
• As shown in the next section, when using this expression as a prior probability times the likelihood in Bayes theorem, the posterior probability turns out to be a beta distribution.
• In Bayesian statistics, the problem arises at that of deciding on a prior probability for the outcome in question ( or when considering multiple outcomes, a prior probability distribution ).
• In Bayesian statistics, the problem arises at that of deciding on a prior probability for the outcome in question ( or when considering multiple outcomes, a prior probability distribution ).
• In fact, " any " particular infinite sequence the immortal monkey types will have " had " a prior probability of 0, even though the monkey must type something.
• This kind of interpretation is often called " Bayesian " because it assumes the writer is also incorporating a prior probability distribution of possible amounts of money in the two envelopes in the switching argument.
• This doesn't sound right, as the doctor must have taken other information, such as the symptoms, age and sex of the patient into consideration, when assigning a prior probability.
• This contrasts with the Bayesian approach, which requires that the hypothesis be assigned a prior probability, which is revised in the light of the observed data to obtain the final probability of the hypothesis.
• Bayesian inference provides a method for updating the probability that a hypothesis is true when presented with some evidence, but it requires a prior probability that the hypothesis is true before the evidence is presented.
• Furthermore, Bayes'rule can be applied iteratively : after observing some evidence, the resulting posterior probability can then be treated as a prior probability, and a new posterior probability computed from new evidence.
• The classical definition enjoyed a revival of sorts due to the general interest in Bayesian probability, because Bayesian methods require a prior probability distribution and the principle of indifference offers one source of such a distribution.
• If a prior probability assignment doesn't " seem right " according to what your intuition tells you, then there must be some " background information " which has not be put into the problem.
• The " Essay " includes theorems of conditional probability which form the basis of what is now called Bayes's Theorem, together with a detailed treatment of the problem of setting a prior probability.
• If a prior probability on \ hat { \ boldsymbol { \ beta } } is known, then a dominate, or outperform, the least squares technique; the best known of these is the James Stein estimator.
• If it is correct that the dynamics " are " time-symmetric, it appears that we need to put in by hand a prior probability that initial configurations with a low thermodynamic entropy are more likely than initial configurations with a high thermodynamic entropy.