Lewis Carroll’s ”Pillow Problems”:
On the 1993 Centenary
Eugene Seneta
Statistical Science, Vol. 8, No. 2, 180-186
Collegio Carlo Alberto
Bayesian Statistics
April 21, 2015
Overview
• Eugene Seneta: Professor Emeritus, School of Mathematics
and Statistics, University of Sydney. He is known for the
variance gamma model in financial mathematics (the
Madan-Seneta process).
• Lewis Carroll: pen name of Charles Lutwidge Dodgson (1832
1898), was an English writer, mathematician, logician and
photographer. His most famous writings are Alice’s
Adventures in Wonderland and its sequel Through the
Looking-Glass → wordplays, logic, and fantasy.
Pillow Problems
• 72 problems: formulated and worked out at night while in
bed, with the answer written down afterwards
some are difficult, interesting and with correct solutions
some are badly posed, with imaginative but incorrect
solutions
• Lennon (1945): ”His range was that of a freshman today in a
good technical school, though the freshman would have
clearer ideas about elementary things than CLD.”
• Weaver (1954): ”He lagged behind the best knowledge of his
time.”
English Probability
• As a probabilist LC may not be not important
→ ”..., but his work reflects the nature, standing and
understanding of probability within the wider English
mathematical community of the time”.
• De Morgan (1086 - 1871): ”inverse probability” (obsolete
term for the probability distribution of an unobserved variable)
→ urn models
• Venn (1834 - 1923): approach to probability through logic
→ improvement of ”Venn diagrams”
• Little or nothing of Laplace (1749 - 1827) and Legendre (1752
- 1833)
No. 45 (1)
• If an infinite number of rods be broken: find he chance that
one at least is broken at the middle.
divide each rod into n + 1 parts, where n is odd.
assume the n points of division are the only (equally likely)
points where a rod can break
⇒ P(rod not breaking in the middle) = 1 − n−1
assume the same number of rods as breakpoints (!)
⇒ P(no rod breaking in the middle) = (1 − n−1)n
⇒ answer = limn→∞ 1 − (1 − n−1)n = 1 − e−1 = 0.6321207
No. 45 (2)
• Density over rod length to the breakpoint position (e.g.
uniform)
⇒ answer = 0
• Countability and uncountability literature at its infancy,
Cantor (1845 - 1918)
• In ignorance of ”continuous probabilities” through probability
densities, Laplace
A Controversy
• Rev. Simmons: a random point being taken on a given line,
what is the chance of it coinciding with a previously assigned
point?
using the uniform distribution: if the point is k, probability
of taking a point to its left is k and to its right is 1 − k
⇒ answer = 0
LC: if the probability of a specific point is zero, the the
probability of any point is zero, yet some point is chosen
⇒ ”... when an event is possible, its chance of happening is
not zero”
No. 50
• There are two bags, H and K, each containing 2 counters:
and it is known that each counter is either black or white. A
white counter is added to bag H, the bag is shaken up, and
one counter is transferred (without looking at it) to bag K,
where the process is repeated, a counter being transferred to
bag H. What is now the chance of drawing a white counter
from bag H?
3 counters in bag H where white can be (0, 1, 2, 3) with
prior distribution (0, 1/4, 1/2, 1/4)
2 counters in bag K where white can be (0, 1, 2, 3) with
prior distribution (1/4, 1/2, 1/4, 0)
a counter is transferred in turn between bags
⇒ correct solution = 17/27 → urn mixing model (Ehrenfest,
Markov chain models)
No. 72 (1)
• a bag contains 2 counters, as to which nothing is known
except that each is either black or white. Ascertain their
colours without taking them out of the bag
No. 72 (2)
• a bag contains 2 counters, as to which nothing is known
except that each is either black or white. Ascertain their
colours without taking them out of the bag
assume a binomial prior distribution: (1/4, 1/2, 1/4) for
(BB, BW, WW)
suppose to add a black counter to the ball
⇒ change of drawing a black counter = 2/3
⇒ there must be two blacks and a white
⇒ before the black was added there must be one black and
one white
No. 72 (3)
• a bag contains 3 counters, as to which nothing is known
except that each is either black or white. Ascertain their
colours without taking them out of the bag
assume a binomial prior distribution: (1/8, 3/8, 3/8, 1/8)
for (BBB, BBW, BWW, WWW)
suppose to add a black counter to the ball
⇒ change of drawing a black counter is
1
8 ∗ 1 + 3
8 ∗ 3
4 + 3
8 ∗ 2
4 + 1
8 ∗ 1
4 = 5
8
5
8 does not coincide with the probability of drawing a black
from any one number of the partition
Thank you for your attention

More Related Content

PPSX
Gr 8O's 2nd Online Session
PPSX
Gr 8O's 2nd Online Session (Updated)
PPTX
Matrices
DOCX
Linear equations reading
PPTX
FM calculus
PDF
1 ESO - UNIT 04 - INTEGER NUMBERS
PPT
Solve Complex Inequalities Algebra 1
DOCX
Guided notes
Gr 8O's 2nd Online Session
Gr 8O's 2nd Online Session (Updated)
Matrices
Linear equations reading
FM calculus
1 ESO - UNIT 04 - INTEGER NUMBERS
Solve Complex Inequalities Algebra 1
Guided notes

Viewers also liked (14)

PDF
Hastings 1970
PDF
Diaconis Ylvisaker 1985
PDF
Presentation of SMC^2 at BISP7
PDF
Berger 2000
PDF
Poster DDP (BNP 2011 Veracruz)
PDF
Species sampling models in Bayesian Nonparametrics
PDF
Bayesian Classics
PDF
Lehmann 1990
PDF
Dependent processes in Bayesian Nonparametrics
PDF
A Gentle Introduction to Bayesian Nonparametrics
PDF
Bayesian Nonparametrics, Applications to biology, ecology, and marketing
PDF
A Gentle Introduction to Bayesian Nonparametrics
PDF
R in latex
PDF
Asymptotics for discrete random measures
Hastings 1970
Diaconis Ylvisaker 1985
Presentation of SMC^2 at BISP7
Berger 2000
Poster DDP (BNP 2011 Veracruz)
Species sampling models in Bayesian Nonparametrics
Bayesian Classics
Lehmann 1990
Dependent processes in Bayesian Nonparametrics
A Gentle Introduction to Bayesian Nonparametrics
Bayesian Nonparametrics, Applications to biology, ecology, and marketing
A Gentle Introduction to Bayesian Nonparametrics
R in latex
Asymptotics for discrete random measures
Ad

Similar to Seneta 1993 (10)

PDF
On reflection
PPT
The-Poincare-Models-PACAPACGroup3Math21.ppt
PPTX
IGCSE Chapter 15 Parpendicular Lines .pptx
PPTX
Chessboard Puzzles Part 2 - Independence
PPT
maths ppt
PPT
Trondheim small (1)
PDF
PDF
Warsaw
PPT
Trondheim small
PPTX
Bell lund-2
On reflection
The-Poincare-Models-PACAPACGroup3Math21.ppt
IGCSE Chapter 15 Parpendicular Lines .pptx
Chessboard Puzzles Part 2 - Independence
maths ppt
Trondheim small (1)
Warsaw
Trondheim small
Bell lund-2
Ad

More from Julyan Arbel (9)

PDF
UCD_talk_nov_2020
PDF
Bayesian neural networks increasingly sparsify their units with depth
PDF
Lindley smith 1972
PDF
Jefferys Berger 1992
PDF
Bayesian Classics
PDF
Arbel oviedo
PDF
Causesof effects
PDF
Bayesian adaptive optimal estimation using a sieve prior
PDF
Seminaire ihp
UCD_talk_nov_2020
Bayesian neural networks increasingly sparsify their units with depth
Lindley smith 1972
Jefferys Berger 1992
Bayesian Classics
Arbel oviedo
Causesof effects
Bayesian adaptive optimal estimation using a sieve prior
Seminaire ihp

Recently uploaded (20)

PDF
Vision Prelims GS PYQ Analysis 2011-2022 www.upscpdf.com.pdf
PDF
1.3 FINAL REVISED K-10 PE and Health CG 2023 Grades 4-10 (1).pdf
PDF
What if we spent less time fighting change, and more time building what’s rig...
PDF
HVAC Specification 2024 according to central public works department
PDF
Journal of Dental Science - UDMY (2021).pdf
PDF
LEARNERS WITH ADDITIONAL NEEDS ProfEd Topic
PDF
semiconductor packaging in vlsi design fab
PDF
Hazard Identification & Risk Assessment .pdf
PDF
Mucosal Drug Delivery system_NDDS_BPHARMACY__SEM VII_PCI.pdf
PPTX
Unit 4 Computer Architecture Multicore Processor.pptx
PPTX
B.Sc. DS Unit 2 Software Engineering.pptx
PDF
FOISHS ANNUAL IMPLEMENTATION PLAN 2025.pdf
PDF
BP 704 T. NOVEL DRUG DELIVERY SYSTEMS (UNIT 1)
PDF
Uderstanding digital marketing and marketing stratergie for engaging the digi...
PDF
Empowerment Technology for Senior High School Guide
PDF
My India Quiz Book_20210205121199924.pdf
PDF
BP 505 T. PHARMACEUTICAL JURISPRUDENCE (UNIT 2).pdf
PPTX
Share_Module_2_Power_conflict_and_negotiation.pptx
PDF
English Textual Question & Ans (12th Class).pdf
PDF
MICROENCAPSULATION_NDDS_BPHARMACY__SEM VII_PCI .pdf
Vision Prelims GS PYQ Analysis 2011-2022 www.upscpdf.com.pdf
1.3 FINAL REVISED K-10 PE and Health CG 2023 Grades 4-10 (1).pdf
What if we spent less time fighting change, and more time building what’s rig...
HVAC Specification 2024 according to central public works department
Journal of Dental Science - UDMY (2021).pdf
LEARNERS WITH ADDITIONAL NEEDS ProfEd Topic
semiconductor packaging in vlsi design fab
Hazard Identification & Risk Assessment .pdf
Mucosal Drug Delivery system_NDDS_BPHARMACY__SEM VII_PCI.pdf
Unit 4 Computer Architecture Multicore Processor.pptx
B.Sc. DS Unit 2 Software Engineering.pptx
FOISHS ANNUAL IMPLEMENTATION PLAN 2025.pdf
BP 704 T. NOVEL DRUG DELIVERY SYSTEMS (UNIT 1)
Uderstanding digital marketing and marketing stratergie for engaging the digi...
Empowerment Technology for Senior High School Guide
My India Quiz Book_20210205121199924.pdf
BP 505 T. PHARMACEUTICAL JURISPRUDENCE (UNIT 2).pdf
Share_Module_2_Power_conflict_and_negotiation.pptx
English Textual Question & Ans (12th Class).pdf
MICROENCAPSULATION_NDDS_BPHARMACY__SEM VII_PCI .pdf

Seneta 1993

  • 1. Lewis Carroll’s ”Pillow Problems”: On the 1993 Centenary Eugene Seneta Statistical Science, Vol. 8, No. 2, 180-186 Collegio Carlo Alberto Bayesian Statistics April 21, 2015
  • 2. Overview • Eugene Seneta: Professor Emeritus, School of Mathematics and Statistics, University of Sydney. He is known for the variance gamma model in financial mathematics (the Madan-Seneta process). • Lewis Carroll: pen name of Charles Lutwidge Dodgson (1832 1898), was an English writer, mathematician, logician and photographer. His most famous writings are Alice’s Adventures in Wonderland and its sequel Through the Looking-Glass → wordplays, logic, and fantasy.
  • 3. Pillow Problems • 72 problems: formulated and worked out at night while in bed, with the answer written down afterwards some are difficult, interesting and with correct solutions some are badly posed, with imaginative but incorrect solutions • Lennon (1945): ”His range was that of a freshman today in a good technical school, though the freshman would have clearer ideas about elementary things than CLD.” • Weaver (1954): ”He lagged behind the best knowledge of his time.”
  • 4. English Probability • As a probabilist LC may not be not important → ”..., but his work reflects the nature, standing and understanding of probability within the wider English mathematical community of the time”. • De Morgan (1086 - 1871): ”inverse probability” (obsolete term for the probability distribution of an unobserved variable) → urn models • Venn (1834 - 1923): approach to probability through logic → improvement of ”Venn diagrams” • Little or nothing of Laplace (1749 - 1827) and Legendre (1752 - 1833)
  • 5. No. 45 (1) • If an infinite number of rods be broken: find he chance that one at least is broken at the middle. divide each rod into n + 1 parts, where n is odd. assume the n points of division are the only (equally likely) points where a rod can break ⇒ P(rod not breaking in the middle) = 1 − n−1 assume the same number of rods as breakpoints (!) ⇒ P(no rod breaking in the middle) = (1 − n−1)n ⇒ answer = limn→∞ 1 − (1 − n−1)n = 1 − e−1 = 0.6321207
  • 6. No. 45 (2) • Density over rod length to the breakpoint position (e.g. uniform) ⇒ answer = 0 • Countability and uncountability literature at its infancy, Cantor (1845 - 1918) • In ignorance of ”continuous probabilities” through probability densities, Laplace
  • 7. A Controversy • Rev. Simmons: a random point being taken on a given line, what is the chance of it coinciding with a previously assigned point? using the uniform distribution: if the point is k, probability of taking a point to its left is k and to its right is 1 − k ⇒ answer = 0 LC: if the probability of a specific point is zero, the the probability of any point is zero, yet some point is chosen ⇒ ”... when an event is possible, its chance of happening is not zero”
  • 8. No. 50 • There are two bags, H and K, each containing 2 counters: and it is known that each counter is either black or white. A white counter is added to bag H, the bag is shaken up, and one counter is transferred (without looking at it) to bag K, where the process is repeated, a counter being transferred to bag H. What is now the chance of drawing a white counter from bag H? 3 counters in bag H where white can be (0, 1, 2, 3) with prior distribution (0, 1/4, 1/2, 1/4) 2 counters in bag K where white can be (0, 1, 2, 3) with prior distribution (1/4, 1/2, 1/4, 0) a counter is transferred in turn between bags ⇒ correct solution = 17/27 → urn mixing model (Ehrenfest, Markov chain models)
  • 9. No. 72 (1) • a bag contains 2 counters, as to which nothing is known except that each is either black or white. Ascertain their colours without taking them out of the bag
  • 10. No. 72 (2) • a bag contains 2 counters, as to which nothing is known except that each is either black or white. Ascertain their colours without taking them out of the bag assume a binomial prior distribution: (1/4, 1/2, 1/4) for (BB, BW, WW) suppose to add a black counter to the ball ⇒ change of drawing a black counter = 2/3 ⇒ there must be two blacks and a white ⇒ before the black was added there must be one black and one white
  • 11. No. 72 (3) • a bag contains 3 counters, as to which nothing is known except that each is either black or white. Ascertain their colours without taking them out of the bag assume a binomial prior distribution: (1/8, 3/8, 3/8, 1/8) for (BBB, BBW, BWW, WWW) suppose to add a black counter to the ball ⇒ change of drawing a black counter is 1 8 ∗ 1 + 3 8 ∗ 3 4 + 3 8 ∗ 2 4 + 1 8 ∗ 1 4 = 5 8 5 8 does not coincide with the probability of drawing a black from any one number of the partition
  • 12. Thank you for your attention