BEGIN:VCALENDAR
PRODID:-//SiteBuilder 2//ÉñÂí¸£ÀûӰƬ ITS Web Team//EN
VERSION:2.0
CALSCALE:GREGORIAN
METHOD:PUBLISH
X-WR-CALNAME:Statistics » Events
X-WR-TIMEZONE:Europe/London
X-LIC-LOCATION:Europe/London
BEGIN:VTIMEZONE
TZID:Europe/London
LAST-MODIFIED:20201010T011803Z
TZURL:http://tzurl.org/zoneinfo/Europe/London
X-LIC-LOCATION:Europe/London
X-PROLEPTIC-TZNAME:LMT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+000115
TZOFFSETTO:+0000
DTSTART:18471201T000000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19160521T020000
RDATE:19170408T020000
RDATE:19180324T020000
RDATE:19190330T020000
RDATE:19200328T020000
RDATE:19210403T020000
RDATE:19220326T020000
RDATE:19230422T020000
RDATE:19240413T020000
RDATE:19270410T020000
RDATE:19300413T020000
RDATE:19330409T020000
RDATE:19340422T020000
RDATE:19350414T020000
RDATE:19380410T020000
RDATE:19390416T020000
RDATE:19400225T020000
RDATE:19460414T020000
RDATE:19470316T020000
RDATE:19480314T020000
RDATE:19490403T020000
RDATE:19530419T020000
RDATE:19540411T020000
RDATE:19570414T020000
RDATE:19600410T020000
RDATE:19680218T020000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19161001T030000
RDATE:19170917T030000
RDATE:19180930T030000
RDATE:19190929T030000
RDATE:19201025T030000
RDATE:19211003T030000
RDATE:19221008T030000
RDATE:19391119T030000
RDATE:19471102T030000
RDATE:19481031T030000
RDATE:19491030T030000
RDATE:19711031T030000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19230916T030000
RRULE:FREQ=YEARLY;UNTIL=19240921T020000Z;BYMONTH=9;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19250419T020000
RRULE:FREQ=YEARLY;UNTIL=19260418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19251004T030000
RRULE:FREQ=YEARLY;UNTIL=19381002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19280422T020000
RRULE:FREQ=YEARLY;UNTIL=19290421T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19310419T020000
RRULE:FREQ=YEARLY;UNTIL=19320417T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19360419T020000
RRULE:FREQ=YEARLY;UNTIL=19370418T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19410504T020000
RDATE:19450402T020000
RDATE:19470413T020000
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19410810T030000
RRULE:FREQ=YEARLY;UNTIL=19430815T010000Z;BYMONTH=8;BYMONTHDAY=9,10,11,12,
 13,14,15;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BDST
TZOFFSETFROM:+0100
TZOFFSETTO:+0200
DTSTART:19420405T020000
RRULE:FREQ=YEARLY;UNTIL=19440402T010000Z;BYMONTH=4;BYMONTHDAY=2,3,4,5,6,7
 ,8;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0200
TZOFFSETTO:+0100
DTSTART:19440917T030000
RDATE:19450715T030000
RDATE:19470810T030000
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19451007T030000
RRULE:FREQ=YEARLY;UNTIL=19461006T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19500416T020000
RRULE:FREQ=YEARLY;UNTIL=19520420T020000Z;BYMONTH=4;BYMONTHDAY=14,15,16,17
 ,18,19,20;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19501022T030000
RRULE:FREQ=YEARLY;UNTIL=19521026T020000Z;BYMONTH=10;BYMONTHDAY=21,22,23,2
 4,25,26,27;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19531004T030000
RRULE:FREQ=YEARLY;UNTIL=19601002T020000Z;BYMONTH=10;BYMONTHDAY=2,3,4,5,6,
 7,8;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19550417T020000
RRULE:FREQ=YEARLY;UNTIL=19560422T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19580420T020000
RRULE:FREQ=YEARLY;UNTIL=19590419T020000Z;BYMONTH=4;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19610326T020000
RRULE:FREQ=YEARLY;UNTIL=19630331T020000Z;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19611029T030000
RRULE:FREQ=YEARLY;UNTIL=19671029T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19640322T020000
RRULE:FREQ=YEARLY;UNTIL=19670319T020000Z;BYMONTH=3;BYMONTHDAY=19,20,21,22
 ,23,24,25;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:BST
TZOFFSETFROM:+0100
TZOFFSETTO:+0100
DTSTART:19681026T230000
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19720319T020000
RRULE:FREQ=YEARLY;UNTIL=19800316T020000Z;BYMONTH=3;BYMONTHDAY=16,17,18,19
 ,20,21,22;BYDAY=SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19721029T030000
RRULE:FREQ=YEARLY;UNTIL=19801026T020000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:DAYLIGHT
TZNAME:BST
TZOFFSETFROM:+0000
TZOFFSETTO:+0100
DTSTART:19810329T010000
RRULE:FREQ=YEARLY;BYMONTH=3;BYDAY=-1SU
END:DAYLIGHT
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19811025T020000
RRULE:FREQ=YEARLY;UNTIL=19891029T010000Z;BYMONTH=10;BYMONTHDAY=23,24,25,2
 6,27,28,29;BYDAY=SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19901028T020000
RRULE:FREQ=YEARLY;UNTIL=19951022T010000Z;BYMONTH=10;BYDAY=4SU
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0000
TZOFFSETTO:+0000
DTSTART:19960101T000000
END:STANDARD
BEGIN:STANDARD
TZNAME:GMT
TZOFFSETFROM:+0100
TZOFFSETTO:+0000
DTSTART:19961027T020000
RRULE:FREQ=YEARLY;BYMONTH=10;BYDAY=-1SU
END:STANDARD
END:VTIMEZONE
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE:20260708
DURATION:P3D
SUMMARY:Compositional Foundations of Statistics & Machine learning Worksh
 op
UID:20260708-8ac672c49c40937a019c4809264c18dd@warwick.ac.uk
CREATED:20260306T132951Z
DESCRIPTION:
LOCATION:B3.03 (Zeeman)
URL:/fac/sci/statistics/news/compositional-foundatio
 ns-of-stats-and-ml2026/
ATTACH:/fac/sci/statistics/news/compositional-founda
 tions-of-stats-and-ml2026/
CATEGORIES:Workshops,CRiSM Workshops,CRiSM
LAST-MODIFIED:20260306T132951Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE-TIME:20270419T090000
DTEND;VALUE=DATE-TIME:20270423T150000
SUMMARY:UK Easter Probability Meeting
TZID:Europe/London
UID:20270419-8ac672c59c03dd43019c09bf20090f5b@warwick.ac.uk
CREATED:20260129T123406Z
DESCRIPTION:
LOCATION:TBC
URL:
ATTACH:
CATEGORIES:Workshops,CRiSM
LAST-MODIFIED:20260129T123406Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE:20260629
DURATION:P5D
SUMMARY:Statistics Summer School at Warwick 2026
UID:20260629-8ac672c699936d39019994c8b2bd0520@warwick.ac.uk
CREATED:20251202T161338Z
DESCRIPTION:LMS Research Summer School in Robust Statistics and Reliable 
 Learning Algorithms Following on from the success of the P@W Summer Scho
 olLink opens in a new window in 2025\, the LMS Research Summer School in
  Robust Statistics and Reliable Learning Algorithms will be held at the 
 ÉñÂí¸£ÀûӰƬ from the 29th June to 3rd July 2026. We gratefully
  acknowledge support from the London Mathematical Society and CRiSM. The
  ambition of the summer school is to expose PhD students and early-caree
 r researchers to research themes at the forefront of robust statistics\,
  broadly interpreted. It will feature three lecture courses and plenary 
 talks by the world's leading experts in the field\, covering topics such
  as: conformal prediction and distribution-free inference\, algorithmic 
 robustness and stability\, differential privacy and data contamination. 
 There will also be the opportunity for attendees to present their work\,
  social events and a summer school dinner.
LOCATION:ÉñÂí¸£ÀûӰƬ\, Zeeman Building\, MS.03
URL:/fac/sci/statistics/news/summerschool2026/
ATTACH:/fac/sci/statistics/news/summerschool2026/
CATEGORIES:Workshops,CRiSM Workshops,CRiSM
LAST-MODIFIED:20251202T161338Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE-TIME:20260528T140000
DTEND;VALUE=DATE-TIME:20260528T150000
SUMMARY:CRiSM colloquium - Nicholas Polson
TZID:Europe/London
UID:20260528-8ac672c69c8a6b40019c8f08bc590b26@warwick.ac.uk
CREATED:20260507T114340Z
DESCRIPTION:Chess has long been a proving ground for AI and statistical r
 easoning. This talk takes a Bayesian look at two recent flashpoints in e
 lite play. The centerpiece is joint work with Shiva Maharaj (Chess Ed) a
 nd Vadim Sokolov (George Mason) on the 2023 Kramnik–Nakamura controversy
 \, in which former world champion Vladimir Kramnik publicly questioned H
 ikaru Nakamura’s 45.5 out of 46 streak in 3+0 online blitz on chess.com.
  Combining Anand’s prior on the prevalence of online cheating with the s
 treak evidence\, we compute a posterior of roughly 99.6% that Nakamura d
 id not cheat. The case study illustrates two classic fallacies — the Pro
 secutor’s Fallacy on Kramnik’s side\, and a misuse of cherry-picking tha
 t violates the likelihood principle on Nakamura’s side — and connects to
  the broader literature on fraud detection and streaks in sports. I will
  then survey related work with the same group: a Brownian-motion model f
 or the probability that Magnus Carlsen reaches an Elo of 2900 and its im
 plications for the K-factor\; a neural-network valuation of (piece\, squ
 are) combinations\; and a comparison of Stockfish and Leela Chess Zero a
 s competing paradigms — handcrafted search versus deep reinforcement lea
 rning — through Plaskett’s endgame study.
LOCATION:MB0.07
CATEGORIES:Colloquium,CRiSM
LAST-MODIFIED:20260507T114340Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE-TIME:20260610T140000
DTEND;VALUE=DATE-TIME:20260610T150000
SUMMARY:CRiSM colloquium - Bin Yu
TZID:Europe/London
UID:20260610-8ac672c69c8a6b40019c8f143d1e0cba@warwick.ac.uk
CREATED:20260224T095634Z
DESCRIPTION:tbc
LOCATION:B3.03
URL:/fac/sci/statistics/news/crismcolloquium/
ATTACH:/fac/sci/statistics/news/crismcolloquium/
CATEGORIES:Colloquium,CRiSM
LAST-MODIFIED:20260224T095634Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
BEGIN:VEVENT
DTSTAMP:20260608T124033Z
DTSTART;VALUE=DATE-TIME:20260622T130000
DTEND;VALUE=DATE-TIME:20260624T140000
SUMMARY:ProbAI Theory of Scaling Laws Workshop 2026
TZID:Europe/London
UID:20260622-8ac672c79add3b14019adfd4a90a0914@warwick.ac.uk
CREATED:20260128T171124Z
DESCRIPTION:Overview Modern neural networks operate at unprecedented scal
 es across model size\, data and compute. A central research problem is t
 o understand how their performance scales with these factors\, which gui
 des how networks can be trained optimally at scale. In recent years\, em
 pirical heuristics for scaling have arguably driven much of the success 
 of Large Language Models (LLMs). Theoretical work on scaling laws has al
 so seen much fruitful progress\, shedding light on empirical phenomena s
 uch as model collapse\, emergence and training stability\, while providi
 ng concrete practical insights on techniques such as hyperparameter tuni
 ng. This three-day workshop will bring together researchers working at t
 he frontiers of theoretical scaling laws to share their insights about t
 he field. The workshop will be the first of its kind in the UK\, inspire
 d by successes of similar workshops in the US and Europe. The first half
  of the workshop consists of introductory tutorials\, with the aim of eq
 uipping attendees with basic tools for framing and understanding problem
 s in this field\; The second half will feature talks on latest research 
 advances. The aim is for researchers across academia and industry to lea
 rn about and participate in this active field of research\, which has se
 en many fruitful empirical outcomes.
LOCATION:ÉñÂí¸£ÀûӰƬ\, Zeeman Building\, MS.01
URL:/fac/sci/statistics/news/probai-scaling-laws-202
 6/
ATTACH:/fac/sci/statistics/news/probai-scaling-laws-
 2026/
CATEGORIES:Workshops,CRiSM Workshops,Applied Probability,CRiSM
LAST-MODIFIED:20260128T171124Z
ORGANIZER;CN=Kathrin Schutrumpf:
END:VEVENT
END:VCALENDAR