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DESCRIPTION:--- This iCal file does *NOT* confirm registration.\r\nEvent d
etails subject to change. ---\r\nhttps://www.statisticswithoutborders.org/
events/4/\r\n\r\nEvent Title: An Introduction to Bayesian Inference\r\nSta
rt Date / Time: Jul 20, 2022 12:00 PM US/Eastern\r\nLocation: Virtual Even
t\r\nSpeaker: Dr. Shirin Golchi\r\nPresenter: Dr. Shirin Golchi, Assistant
Professor, McGill University, Canada\r\nThis webinar will provide an intr
oduction to basic concepts in Bayesian inference. Topics that will be cove
red include essential components of Bayesian statistics, estimation and un
certainty quantification in single and multi- parameter linear and general
ized linear models, as well as a brief introduction to Bayesian hierarchic
al modeling and Bayesian computation. The workshop will include examples o
f parametric inference in R using R-packages that rely on Stan (rstanarm a
nd brms). At the end of this workshops participants will be able to: 1) Sp
ecify simple Bayesian models, 2) Make Bayesian inference in single paramet
er models, and 3) Fit linear and generalized linear models using rstanarm
or brms.--- This iCal file does *NOT* confirm registration.Event details s
ubject to change. ---\r\n\r\n--- By Tendenci - The Open Source AMS for Ass
ociations ---\r\n
UID:uid4@statisticswithoutborders.org
SUMMARY:An Introduction to Bayesian Inference
DTSTART:20220720T160000Z
DTEND:20220720T180000Z
CLASS:PUBLIC
PRIORITY:5
DTSTAMP:20240418T010555Z
TRANSP:OPAQUE
SEQUENCE:0
LOCATION:Virtual Event
X-ALT-DESC;FMTTYPE=text/html:--- This iCal file does *NOT* confirm re
gistration.Event details subject to change. ---

# Event Title: An I
ntroduction to Bayesian Inference

https://www.statisticswithoutbo
rders.org/events/4/

When: Jul 20, 2022 12:00 PM US/Eastern

Speaker: Dr. Shirin Golchi

**Presenter
: Dr. Shirin Golchi, Assistant Professor, McGill University, Canada**

<
/strong>

** **This webinar will provide an introduction to basic concept
s in Bayesian inference. Topics that will be covered include essential com
ponents of Bayesian statistics, estimation and uncertainty quantification
in single and multi- parameter linear and generalized linear models, as we
ll as a brief introduction to Bayesian hierarchical modeling and Bayesian
computation. The workshop will include examples of parametric inference in
R using R-packages that rely on Stan (rstanarm and brms). At the end of t
his workshops participants will be able to: 1) Specify simple Bayesian mod
els, 2) Make Bayesian inference in single parameter models, and 3) Fit lin
ear and generalized linear models using rstanarm or brms.

**--
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change. ---**

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**