Silicon Investor (SI) -- The First Internet Community

STOCKTALK

We've detected that you're using an ad content blocking browser plug-in or feature. Ads provide a critical source of revenue to the continued operation of Silicon Investor. We ask that you disable ad blocking while on Silicon Investor in the best interests of our community. If you are not using an ad blocker but are still receiving this message, make sure your browser's tracking protection is set to the 'standard' level.

Technology Stocks : Qualcomm Incorporated (QCOM) -- Ignore unavailable to you. Want to Upgrade?

To: Jim Willie CB who wrote (35933)	7/19/1999 9:19:00 PM
From: RoseCampion	Read Replies (5) \| Respond to of 152472

Jim: I like this growing list - "The Six 'S's of Q" - something to keep my Spirits up while waiting for the open tomorrow: Siemens Sony Sprint S&P addition Shorts Split Anyone have others? -SRose-

To: Jim Willie CB who wrote (35933)	7/19/1999 9:43:00 PM
From: Catcher	Respond to of 152472

agree the shortage will work itself out in short order. the competing parts mfgs will attack the opportunity to rescue qcom

To: Jim Willie CB who wrote (35933)	7/19/1999 10:37:00 PM
From: Morgan Drake	Respond to of 152472

Hey Smartypants Jim Willie. Here ya go pal. Bayesian Analysis -- Bayesian methods (so called after the English mathematician Thomas Bayes) provide alternatives that allow one to combine prior information about a population parameter with information contained in a sample to guide the statistical inference process. A prior probability distribution for a parameter of interest is specified first. Sample information is then obtained and combined through an application of Bayes's theorem to provide a posterior probability distribution for the parameter. The posterior distribution provides the basis for statistical inferences concerning the parameter. A key, and somewhat controversial, feature of Bayesian methods is the notion of a probability distribution for a population parameter. According to classical statistics, parameters are constants and cannot be represented as random variables. Bayesian proponents argue that, if a parameter value is unknown, then it makes sense to specify a probability distribution that describes the possible values for the parameter as well as their likelihood. The Bayesian approach permits the use of objective data or subjective opinion in specifying a prior distribution. With the Bayesian approach, different individuals might specify different prior distributions. Classical statisticians argue that for this reason Bayesian methods suffer from a lack of objectivity. Bayesian proponents argue that the classical methods of statistical inference have built-in subjectivity (through the choice of a sampling plan) and that the advantage of the Bayesian approach is that the subjectivity is made explicit. Bayesian methods have been used extensively in statistical decision theory. In this context, Bayes's theorem provides a mechanism for combining a prior probability distribution for the states of nature with sample information to provide a revised (posterior) probability distribution about the states of nature. These posterior probabilities are then used to make better decisions. Morgan