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Related to priors: Prior probability


n. slang for a criminal defendant's previous record of criminal charges, convictions, or other judicial disposal of criminal cases (such as probation, dismissal or acquittal). Only previous felony convictions can be introduced into evidence. However, the record of "priors" can have an impact on sentencing, as with prior drunk driving convictions requiring mandatory jail sentences, and "three strikes, you're out," providing for extended sentences for the third felony conviction.

References in periodicals archive ?
Perhaps the Justices on the Supreme Court realize all this and find it impossible to block jurors from inferring unmentioned priors, but still think that officially excluding priors is preferable to admitting them since the former policy keeps the judges' hands clean.
In the resultant scope for speculation, it is thus capable of engendering as much or more prejudice against him [as the admission of priors would].
(4) As it turns out, this is not a dilemma for the defendant with priors but it is for the defendant without priors, as the failure to testify is highly associated with guilty verdicts.
(11) That compares with the mean acquittal rate in all the trials in the NCSC study (including those of defendants without priors) of about three-in-ten.
Berger and Sun [4] have discussed the Bayesian analysis for a poly Weibull distribution using informative priors and also through light on the Bayesian computations and simulation by using the procedure of Gibbs sampling.
It is also focus on the Bayes estimation for the Weibull distribution when scale and shape parameter(s) are unknown by using joint informative priors i.e.
In our study priors are quite flexible but in general set up it is not possible to obtain the Bayes estimates in explicit form.
The Bayes estimates and Bayes posterior risk for the parameters are obtained under four loss functions using informative and Non-informative priors.
This study provides a Bayesian analysis of the time-to-failure model using informative (Gamma) and uninformative (Jeffreys) priors. A method is also given to elicit the hyperparameters of the prior density for the parameters of the said model.
The comparison of the informative and non- informative priors with respect to posterior variance, Bayesian interval estimate, coefficient of skewness for posterior distribution and Bayes posterior risk is presented.
The Posterior Distribution using the Non-Informative Priors (NIP): A non-informative prior has been suggested by Jeffreys (1946, 1961), which is frequently used in situation where one does not have much information about the parameters.
Comparison of Priors with respect to Posterior Variance: The Posterior variance of parameter l is given in Table 1 which reveals that informative prior provides more efficient estimates.