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PRODID;X-RICAL-TZSOURCE=TZINFO:-//Calagator//EN
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X-WR-CALNAME:Calagator
METHOD:PUBLISH
VERSION:2.0
BEGIN:VTIMEZONE
TZID;X-RICAL-TZSOURCE=TZINFO:America/Los_Angeles
BEGIN:DAYLIGHT
DTSTART:20180311T020000
RDATE:20180311T020000
TZOFFSETFROM:-0800
TZOFFSETTO:-0700
TZNAME:PDT
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BEGIN:STANDARD
DTSTART:20181104T020000
RDATE:20181104T020000
TZOFFSETFROM:-0700
TZOFFSETTO:-0800
TZNAME:PST
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BEGIN:VEVENT
CREATED;VALUE=DATE-TIME:20180829T220429Z
DTEND;TZID=America/Los_Angeles;VALUE=DATE-TIME:20180919T200000
DTSTART;TZID=America/Los_Angeles;VALUE=DATE-TIME:20180919T180000
DTSTAMP;VALUE=DATE-TIME:20180829T220429Z
LAST-MODIFIED;VALUE=DATE-TIME:20180831T072815Z
UID:http://calagator.org/events/1250474187
DESCRIPTION:At this meeting we will have:&#13\;\n- an intro/review of pro
 bability distributions and where to use them&#13\;\n- a discussion on a 
 real life case study&#13\;\n- a discussion on chapters 1-3 of the book.&
 #13\;\n&#13\;\nCome on out and learn\, teach and discuss probabilistic p
 rogramming.\n\nTags: bayesian statistics\, probabilistic programming\, p
 ymc3\, stan\, pyro\, tensorflow\n\nImported from: http://calagator.org/e
 vents/1250474187
URL:https://www.meetup.com/Probabilistic-Programming/events/254219283/
SUMMARY:Peer mentoring\, probability distributions and case study
LOCATION:The Tech Academy: 310 SW 4th Ave Suite 230\, Portland OR 97204 u
 s
SEQUENCE:3
END:VEVENT
BEGIN:VEVENT
CREATED;VALUE=DATE-TIME:20181107T202640Z
DTEND;TZID=America/Los_Angeles;VALUE=DATE-TIME:20181114T200000
DTSTART;TZID=America/Los_Angeles;VALUE=DATE-TIME:20181114T180000
DTSTAMP;VALUE=DATE-TIME:20181107T202640Z
LAST-MODIFIED;VALUE=DATE-TIME:20181107T202640Z
UID:http://calagator.org/events/1250474547
DESCRIPTION:This month we'll focus a bit more on the 'programming' part o
 f Probabilisitic Programming. First we'll have:&#13\;\n&#13\;\nProbabili
 stic programming from scratch&#13\;\n&#13\;\nReal world observational da
 ta is always imperfect or incomplete in some way. Those limitations mean
  that what we learn from our data is somewhat uncertain. We want to fill
  in the blanks to the extent possible and be able to say how confident w
 e are as we do this. This is inference. Probabilistic programming makes 
 it easier to learn from data. Let’s see how to build a basic probabilist
 ic programming system from scratch in Python to introduce Approximate Ba
 yesian Computation (ABC)\, which is a specific\, extremely simple algori
 thm to perform Bayesian inference.&#13\;\n&#13\;\nBio: David is a Busine
 ss Analyst and Software Developer with six years of corporate experience
  in building databases\, managing and analyzing large data sets\, and op
 timizing systems and processes. Machine Learning enthusiast focused on N
 atural Language Processing (NLP) and visual recognition.&#13\;\n&#13\;\n
 Then we'll introduce and discuss &quot\;An Introduction to Probabilistic
  Programming&quot\; https://arxiv.org/abs/1809.10756 and explore some of
  the concepts we've covered using Pyro http://pyro.ai\n\nTags: machine l
 earning\, probabilistic programming\, pyro\n\nImported from: http://cala
 gator.org/events/1250474547
URL:https://www.meetup.com/Probabilistic-Programming/events/nzllgqyxpbcc/
SUMMARY:Probabilistic programming from scratch w/ David Molina
LOCATION:The Tech Academy: 310 SW 4th Ave Suite 230\, Portland OR 97204 u
 s
SEQUENCE:1
END:VEVENT
END:VCALENDAR