BEGIN:VCALENDAR
VERSION:2.0
PRODID:-//jEvents 2.0 for Joomla//EN
CALSCALE:GREGORIAN
METHOD:PUBLISH
BEGIN:VTIMEZONE
TZID:America/New_York
BEGIN:STANDARD
DTSTART:20241103T010000
RDATE:20250309T030000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:America/New_York EST
END:STANDARD
BEGIN:STANDARD
DTSTART:20251102T010000
RDATE:20260308T030000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:America/New_York EST
END:STANDARD
BEGIN:STANDARD
DTSTART:20261101T010000
RDATE:20270314T030000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:America/New_York EST
END:STANDARD
BEGIN:STANDARD
DTSTART:20271107T010000
RDATE:20280312T030000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:America/New_York EST
END:STANDARD
BEGIN:STANDARD
DTSTART:20281105T010000
RDATE:20290311T030000
TZOFFSETFROM:-0400
TZOFFSETTO:-0500
TZNAME:America/New_York EST
END:STANDARD
BEGIN:DAYLIGHT
DTSTART:20240323T140000
RDATE:20241103T010000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:America/New_York EDT
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20250309T030000
RDATE:20251102T010000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:America/New_York EDT
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20260308T030000
RDATE:20261101T010000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:America/New_York EDT
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20270314T030000
RDATE:20271107T010000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:America/New_York EDT
END:DAYLIGHT
BEGIN:DAYLIGHT
DTSTART:20280312T030000
RDATE:20281105T010000
TZOFFSETFROM:-0500
TZOFFSETTO:-0400
TZNAME:America/New_York EDT
END:DAYLIGHT
END:VTIMEZONE
BEGIN:VEVENT
UID:74082e8faa6f0a808629c3bbe44f8866
CATEGORIES:Discrete Math
CREATED:20250320T121802
SUMMARY:Wesley Pegden - Probability spaces driven by geometric constraints
LOCATION:Hill 705
DESCRIPTION:Speaker: Wesley Pegden (https://www.math.cmu.edu/~wes/) (CMU)\nTitle: Proba
 bility spaces driven by geometric constraints\nAbstract: What can we unders
 tand about probability spaces on "nice" partitions of a geometric region?  
 Can we design efficient samplers for geometric partitions of a region? Can 
 we at least detect extreme outliers?  These questions have become particula
 rly salient in the past several years as the techniques developed by mathem
 aticians are now applied to conduct statistical analyses of things like U.S
 . political districtings.  We will discuss some recent developments on prob
 ability spaces defined by geometric constraints, including positive and neg
 ative results on the mixing times of relevant Markov chains, Markov chain m
 ethods which eschew mixing-time requirements, and direct sampling methods.\
 n
X-ALT-DESC;FMTTYPE=text/html:<p dir="ltr" style="line-height: 1.38; margin-top: 9pt; margin-bottom: 0pt;
 "><span style="font-size: 11pt; font-family: Lato; color: #000000; backgrou
 nd-color: transparent; font-weight: bold; font-style: normal; font-variant:
  normal; text-decoration: none; vertical-align: baseline; white-space: pre-
 wrap;">Speaker:</span><span style="font-size: 10pt; font-family: Lato; colo
 r: #000000; background-color: transparent; font-weight: bold; font-style: n
 ormal; font-variant: normal; text-decoration: none; vertical-align: baselin
 e; white-space: pre-wrap;"> </span><a href="https://www.math.cmu.edu/~wes/"
  style="text-decoration: none;"><span style="font-size: 11pt; font-family: 
 Lato; color: #cc0000; background-color: transparent; font-weight: 400; font
 -style: normal; font-variant: normal; text-decoration: underline; vertical-
 align: baseline; white-space: pre-wrap;">Wesley Pegden</span></a><span styl
 e="font-size: 11pt; font-family: Lato; color: #000000; background-color: tr
 ansparent; font-weight: 400; font-style: normal; font-variant: normal; text
 -decoration: none; vertical-align: baseline; white-space: pre-wrap;"> (CMU)
 </span></p><p dir="ltr" style="line-height: 1.38; margin-top: 9pt; margin-b
 ottom: 10pt;"><span style="font-size: 11pt; font-family: Lato; color: #0000
 00; background-color: transparent; font-weight: bold; font-style: normal; f
 ont-variant: normal; text-decoration: none; vertical-align: baseline; white
 -space: pre-wrap;">Title</span><span style="font-size: 11pt; font-family: L
 ato; color: #000000; background-color: transparent; font-weight: 400; font-
 style: normal; font-variant: normal; text-decoration: none; vertical-align:
  baseline; white-space: pre-wrap;">: Probability spaces driven by geometric
  constraints</span></p><p dir="ltr" style="line-height: 1.38; margin-top: 9
 pt; margin-bottom: 0pt;"><span style="font-size: 11pt; font-family: Lato; c
 olor: #000000; background-color: transparent; font-weight: bold; font-style
 : normal; font-variant: normal; text-decoration: none; vertical-align: base
 line; white-space: pre-wrap;">Abstract</span><span style="font-size: 11pt; 
 font-family: Lato; color: #000000; background-color: transparent; font-weig
 ht: 400; font-style: normal; font-variant: normal; text-decoration: none; v
 ertical-align: baseline; white-space: pre-wrap;">: What can we understand a
 bout probability spaces on "nice" partitions of a geometric region?&nbsp; C
 an we design efficient samplers for geometric partitions of a region? Can w
 e at least detect extreme outliers?&nbsp; These questions have become parti
 cularly salient in the past several years as the techniques developed by ma
 thematicians are now applied to conduct statistical analyses of things like
  U.S. political districtings.&nbsp; We will discuss some recent development
 s on probability spaces defined by geometric constraints, including positiv
 e and negative results on the mixing times of relevant Markov chains, Marko
 v chain methods which eschew mixing-time requirements, and direct sampling 
 methods.</span></p>
DTSTAMP:20260827T115848
DTSTART;TZID=America/New_York:20250324T140000
DTEND;TZID=America/New_York:20250324T150000
SEQUENCE:0
TRANSP:OPAQUE
END:VEVENT
END:VCALENDAR