![]() Some of the data that are collected include the number of visitors, their source, and the pages they visit anonymously. ![]() Installed by Google Analytics, _gid cookie stores information on how visitors use a website, while also creating an analytics report of the website's performance. The pattern element in the name contains the unique identity number of the account or website it relates to. This cookie is installed by Google Analytics.Ī variation of the _gat cookie set by Google Analytics and Google Tag Manager to allow website owners to track visitor behaviour and measure site performance. ![]() The cookie stores information anonymously and assigns a randomly generated number to recognize unique visitors. The _ga cookie, installed by Google Analytics, calculates visitor, session and campaign data and also keeps track of site usage for the site's analytics report. It contains the domain, initial timestamp (first visit), last timestamp (last visit), current timestamp (this visit), and session number (increments for each subsequent session). This is the main cookie set by Hubspot, for tracking visitors. The _hssrc cookie set to 1 indicates that the user has restarted the browser, and if the cookie does not exist, it is assumed to be a new session. This cookie is set by Hubspot whenever it changes the session cookie. HubSpot sets this cookie to keep track of sessions and to determine if HubSpot should increment the session number and timestamps in the _hstc cookie. These cookies help provide information on metrics the number of visitors, bounce rate, traffic source, etc. It does not store any personal data.Īnalytical cookies are used to understand how visitors interact with the website. The cookie is set by the GDPR Cookie Consent plugin and is used to store whether or not user has consented to the use of cookies. It works only in coordination with the primary cookie. Records the default button state of the corresponding category & the status of CCPA. The cookie is used to store the user consent for the cookies in the category "Performance". This cookie is set by GDPR Cookie Consent plugin. The cookie is used to store the user consent for the cookies in the category "Other. The cookies is used to store the user consent for the cookies in the category "Necessary". The cookie is set by GDPR cookie consent to record the user consent for the cookies in the category "Functional". The cookie is used to store the user consent for the cookies in the category "Analytics". Set by the GDPR Cookie Consent plugin, this cookie is used to record the user consent for the cookies in the "Advertisement" category. These cookies ensure basic functionalities and security features of the website, anonymously. Also, my ultimate goal is to also get IG based on a MVN model of more than one trait (i.e., stature and weight).Necessary cookies are absolutely essential for the website to function properly. I assume I’d have to include the target somewhere. Further, X here is not the target variable from above, it is a covariate age. Here I include a VERY general model that models stature as a normal distribution with a mean function and sd function. Either extracting the results OR possibly embedded in generated quantities? Math aside, does anyone have any suggestions on how the Stan log probability density can be used here. Given stature is continuous, entropy is equal to -\int_ f(x,y)logf(x|y) dxdy. To be precise IG = H(x) - H(x|y) or the entropy of the target variable minus the conditional entropy of x given y. My ultimate goal is to learn the information gain i.e., how much more certain are we of an individual’s country of origin given their stature. Here’s the background - I have a feature vector of response variables y that is say a measurement of an individual’s stature and my target or conditioning variable say x is the country that individual comes from. ![]() I have a question related to the application of Stan results to downstream information theoretic metrics including entropy, conditional entropy, and information gain. ![]()
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