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Frontiers of Statistics and Forecasting in Celebration of the 80Th Birthday of George C
Frontiers of Statistics and Forecasting in Celebration of the 80th Birthday of George C. Tiao Humanities and Social Sciences Building Academia Sinica, Taipei, Taiwan December 17~18, 2013 The Chinese Institute of Probability and Statistics Institute of Economics, Academia Sinica Institute of Statistical Science, Academia Sinica DGBAS, Executive Yuan, R.O.C. Welcome Dear Friends: On behalf of the organizing committee, it is my great privilege and honor to welcome you to the conference “Frontiers of Statistics and Forecasting” to celebrate the 80th birthday of Professor George C. Tiao. A leading figure in statistics, George has had a great impact on many econometricians and statisticians around the world, especially in the Chinese community. It is particularly fitting to have the celebration hosted by the Academia Sinica and sponsored jointed by The Chinese Institute of Probability and Statistics, Institute of Economics, Institute of Statistical Science, and the Directorate General of Budget, Accounting and Statistics. Many of you took a long journey, including from Sao Paulo, London, Madrid, Illinois, New Jersey, Washington DC, Beijing, Hong Kong, Shanghai, and Tokyo, to join the celebration. To you, we say “thank you”. In the next two days, you will hear many of George’s innovative contributions in statistics and economics. However, the conference cannot cover all of George’s contributions. He is instrumental in the developments of statistical education and research in Taiwan, Beijing, and Hong Kong, and in the establishment of the International Chinese Statistical Association. He is also the founding chair-editor of Statistica Sinica. He has co-established both the annual NBER/NSF Time Series Conference and the Conference for Making Statistics More Effective in Schools of Business. -
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A
Memorial to Sir Harold Jeffreys 1891-1989 JOHN A. HUDSON and ALAN G. SMITH University of Cambridge, Cambridge, England Harold Jeffreys was one of this century’s greatest applied mathematicians, using mathematics as a means of under standing the physical world. Principally he was a geo physicist, although statisticians may feel that his greatest contribution was to the theory of probability. However, his interest in the latter subject stemmed from his realization of the need for a clear statistical method of analysis of data— at that time, travel-time readings from seismological stations across the world. He also made contributions to astronomy, fluid dynamics, meteorology, botany, psychol ogy, and photography. Perhaps one can identify Jeffreys’s principal interests from three major books that he wrote. His mathematical skills are displayed in Methods of Mathematical Physics, which he wrote with his wife Bertha Swirles Jeffreys and which was first published in 1946 and went through three editions. His Theory o f Probability, published in 1939 and also running to three editions, espoused Bayesian statistics, which were very unfashionable at the time but which have been taken up since by others and shown to be extremely powerful for the analysis of data and, in particular, image enhancement. However, the book for which he is probably best known is The Earth, Its Origin, History and Physical Consti tution, a broad-ranging account based on observations analyzed with care, using mathematics as a tool. Jeffreys’s scientific method (now known as Inverse Theory) was a logical process, clearly stated in another of his books, Scientific Inference. -
Probing the Dynamics of the Imine-Based Pentafoil Knot and Pentameric Circular Helicate Assembly † ‡ ‡ § ‡ † ‡ Jean-Francoiş Ayme, , Jonathon E
This is an open access article published under a Creative Commons Attribution (CC-BY) License, which permits unrestricted use, distribution and reproduction in any medium, provided the author and source are cited. Article Cite This: J. Am. Chem. Soc. 2019, 141, 3605−3612 pubs.acs.org/JACS Probing the Dynamics of the Imine-Based Pentafoil Knot and Pentameric Circular Helicate Assembly † ‡ ‡ § ‡ † ‡ Jean-Francoiş Ayme, , Jonathon E. Beves, , Christopher J. Campbell, and David A. Leigh*, , † School of Chemistry, University of Manchester, Oxford Road, Manchester M13 9PL, United Kingdom ‡ School of Chemistry, University of Edinburgh, The King’s Buildings, West Mains Road, Edinburgh EH9 3JJ, United Kingdom *S Supporting Information ABSTRACT: We investigate the self-assembly dynamics of an imine-based pentafoil knot and related pentameric circular helicates, each derived from a common bis(formylpyridine)- bipyridyl building block, iron(II) chloride, and either monoamines or a diamine. The mixing of circular helicates derived from different amines led to the complete exchange of the N-alkyl residues on the periphery of the metallo-supramolecular scaffolds over 4 days in DMSO at 60 °C. Under similar conditions, deuterium-labeled and nonlabeled building blocks showed full dialdehyde building block exchange over 13 days for open circular helicates but was much slower for the analogous closed-loop pentafoil knot (>60 days). Although both knots and open circular helicates self-assemble under thermodynamic control given sufficiently long reaction times, this is significantly longer than the time taken to afford the maximum product yield (2 days). Highly effective error correction occurs during the synthesis of imine-based pentafoil molecular knots and pentameric circular helicates despite, in practice, the systems not operating under full thermodynamic control. -
A Parsimonious Tour of Bayesian Model Uncertainty
A Parsimonious Tour of Bayesian Model Uncertainty Pierre-Alexandre Mattei Université Côte d’Azur Inria, Maasai project-team Laboratoire J.A. Dieudonné, UMR CNRS 7351 e-mail: [email protected] Abstract: Modern statistical software and machine learning libraries are enabling semi-automated statistical inference. Within this context, it appears eas- ier and easier to try and fit many models to the data at hand, thereby reversing the Fisherian way of conducting science by collecting data after the scientific hypothesis (and hence the model) has been determined. The renewed goal of the statistician becomes to help the practitioner choose within such large and heterogeneous families of models, a task known as model selection. The Bayesian paradigm offers a systematized way of as- sessing this problem. This approach, launched by Harold Jeffreys in his 1935 book Theory of Probability, has witnessed a remarkable evolution in the last decades, that has brought about several new theoretical and methodological advances. Some of these recent developments are the focus of this survey, which tries to present a unifying perspective on work carried out by different communities. In particular, we focus on non-asymptotic out-of-sample performance of Bayesian model selection and averaging tech- niques, and draw connections with penalized maximum likelihood. We also describe recent extensions to wider classes of probabilistic frameworks in- cluding high-dimensional, unidentifiable, or likelihood-free models. Contents 1 Introduction: collecting data, fitting many models . .2 2 A brief history of Bayesian model uncertainty . .2 3 The foundations of Bayesian model uncertainty . .3 3.1 Handling model uncertainty with Bayes’s theorem . -
Weak Functional Group Interactions Revealed Through Metal-Free Active Template Rotaxane Synthesis
ARTICLE https://doi.org/10.1038/s41467-020-14576-7 OPEN Weak functional group interactions revealed through metal-free active template rotaxane synthesis Chong Tian 1,2, Stephen D.P. Fielden 1,2, George F.S. Whitehead 1, Iñigo J. Vitorica-Yrezabal1 & David A. Leigh 1* 1234567890():,; Modest functional group interactions can play important roles in molecular recognition, catalysis and self-assembly. However, weakly associated binding motifs are often difficult to characterize. Here, we report on the metal-free active template synthesis of [2]rotaxanes in one step, up to 95% yield and >100:1 rotaxane:axle selectivity, from primary amines, crown ethers and a range of C=O, C=S, S(=O)2 and P=O electrophiles. In addition to being a simple and effective route to a broad range of rotaxanes, the strategy enables 1:1 interactions of crown ethers with various functional groups to be characterized in solution and the solid state, several of which are too weak — or are disfavored compared to other binding modes — to be observed in typical host–guest complexes. The approach may be broadly applicable to the kinetic stabilization and characterization of other weak functional group interactions. 1 Department of Chemistry, University of Manchester, Manchester M13 9PL, UK. 2These authors contributed equally: Chong Tian, Stephen D. P. Fielden. *email: [email protected] NATURE COMMUNICATIONS | (2020) 11:744 | https://doi.org/10.1038/s41467-020-14576-7 | www.nature.com/naturecommunications 1 ARTICLE NATURE COMMUNICATIONS | https://doi.org/10.1038/s41467-020-14576-7 he bulky axle end-groups of rotaxanes mechanically lock To explore the scope of this unexpected method of rotaxane Trings onto threads, preventing the dissociation of the synthesis, here we carry out a study of the reaction with a series of components even if the interactions between them are not related electrophiles. -
Volume 70, No. 3, October 2006
Inside Volume 70, No.3, October 2006 Articles and Features 70 Structure Determination of New Algal Toxins using NMR Methods Alistair L. Wilkins*,a and Christopher O. Milesb,c 74 Faecal Sterols and Fluorescent Whiteners as Indicators of the Source of Faecal Contamination Megan Devane,* Darren Saunders and Brent Gilpin 78 The Curious Case of Phosphate Solubility Andrew J. Pratt 82 Water Oxidation by Ruthenium Dimers Matthew I. J. Polson 85 Fluorinated Analogues of Biological Molecules: Accessing New Chemical, Physical and Biological Properties Michael Edmondsa and Victoria Peddieb 88 Drug Discovery in the New Millennium Steven G. Aitken 93 2006 NZIC Salary Survey Summary 98 Chemical Processes In New Zealand – Where To From Here? 101 38th International Chemistry Olympiad Regular Columns 99 Patent Proze 102 NZIC News 108 Conference Calendar Advertisers Index Cover Shimadzu Scientific Instruments Ltd Back Cover Shimadzu Scientific Instruments Ltd Inside Front Cover Global Science Inside Back Cover Pacific Laboratory Products 81 NZIC Conference 84 Graham B Jackson: Certified Reference Materials Disclaimer The New Zealand Institute of Scientific Editor The views and opinions expressed in Chemistry Incorporated Professor B. Halton, FRSNZ, Hon, FNZIC Chemistry in New Zealand are those of the PO Box 39-112 School of Chemical and Physical Sciences individual authors and are not necessarily those Harewood, Christchurch Victoria University of the publisher, the Editorial Board or the PO Box 600 Phone: +64 3 359 7275 New Zealand Institute of Chemistry. Whilst Wellington, New Zealand Fax: +64 3 359 7248 the publisher has taken every precaution to Email: NZIC.Offi[email protected] Phone: +64 4 463 5954 ensure the total accuracy of material contained Fax: +64 4 463 5241 in Chemistry in New Zealand, no responsibility Managing Editors and Publishers Email: [email protected] for errors or omissions will be accepted. -
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK
School of Social Sciences Economics Division University of Southampton Southampton SO17 1BJ, UK Discussion Papers in Economics and Econometrics Professor A L Bowley’s Theory of the Representative Method John Aldrich No. 0801 This paper is available on our website http://www.socsci.soton.ac.uk/economics/Research/Discussion_Papers ISSN 0966-4246 Key names: Arthur L. Bowley, F. Y. Edgeworth, , R. A. Fisher, Adolph Jensen, J. M. Keynes, Jerzy Neyman, Karl Pearson, G. U. Yule. Keywords: History of Statistics, Sampling theory, Bayesian inference. Professor A. L. Bowley’s Theory of the Representative Method * John Aldrich Economics Division School of Social Sciences University of Southampton Southampton SO17 1BJ UK e-mail: [email protected] Abstract Arthur. L. Bowley (1869-1957) first advocated the use of surveys–the “representative method”–in 1906 and started to conduct surveys of economic and social conditions in 1912. Bowley’s 1926 memorandum for the International Statistical Institute on the “Measurement of the precision attained in sampling” was the first large-scale theoretical treatment of sample surveys as he conducted them. This paper examines Bowley’s arguments in the context of the statistical inference theory of the time. The great influence on Bowley’s conception of statistical inference was F. Y. Edgeworth but by 1926 R. A. Fisher was on the scene and was attacking Bayesian methods and promoting a replacement of his own. Bowley defended his Bayesian method against Fisher and against Jerzy Neyman when the latter put forward his concept of a confidence interval and applied it to the representative method. * Based on a talk given at the Sample Surveys and Bayesian Statistics Conference, Southampton, August 2008. -
The Enigma of Karl Pearson and Bayesian Inference
The Enigma of Karl Pearson and Bayesian Inference John Aldrich 1 Introduction “An enigmatic position in the history of the theory of probability is occupied by Karl Pearson” wrote Harold Jeffreys (1939, p. 313). The enigma was that Pearson based his philosophy of science on Bayesian principles but violated them when he used the method of moments and probability-values in statistics. It is not uncommon to see a divorce of practice from principles but Pearson also used Bayesian methods in some of his statistical work. The more one looks at his writings the more puzzling they seem. In 1939 Jeffreys was probably alone in regretting that Pearson had not been a bottom to top Bayesian. Bayesian methods had been in retreat in English statistics since Fisher began attacking them in the 1920s and only Jeffreys ever looked for a synthesis of logic, statistical methods and everything in between. In the 1890s, when Pearson started, everything was looser: statisticians used inverse arguments based on Bayesian principles and direct arguments based on sampling theory and a philosopher-physicist-statistician did not have to tie everything together. Pearson did less than his early guide Edgeworth or his students Yule and Gosset (Student) to tie up inverse and direct arguments and I will be look- ing to their work for clues to his position and for points of comparison. Of Pearson’s first course on the theory of statistics Yule (1938, p. 199) wrote, “A straightforward, organized, logically developed course could hardly then [in 1894] exist when the very elements of the subject were being developed.” But Pearson never produced such a course or published an exposition as integrated as Yule’s Introduction, not to mention the nonpareil Probability of Jeffreys. -
Mathematical Statistics and the ISI (1885-1939)
Mathematical Statistics and the ISI (1885-1939) Stephen M. Stigler University of Chicago, Department of Statistics 5734 University Avenue Chicago IL 60637, USA [email protected] 1. Introduction From its inception in 1885, the International Statistical Institute took all of statistics as its province. But the understood meaning of “statistics” has undergone significant change over the life of the Institute, and it is of interest to inquire how that change, whether evolutionary or revolutionary, has been reflected in the business of the Institute, in its meetings and its membership. I propose to consider this question in regard to one particular dimension, that of mathematical statistics, and one particular period, that from the rebirth of international statistics in 1885 until the eve of World War II. Mathematical statistics has been a recognized division of statistics at least since the 1860s, when Quetelet at the Congress in the Hague admonished the assembled statisticians not to divide the école mathématique from the école historique, but to take statistics whole, to open the doors to statistics in the broadest sense in all its flavors (Stigler, 2000). But even with this recognition, mathematical statistics only emerged as a discipline later. I have argued elsewhere that this emergence can be dated to the year 1933, and even if one thinks that date absurdly precise, there was a distinct and palpable change that was evident around that time (Stigler, 1999, Ch. 8). It was then that changes initiated a half-century earlier by Galton and Edgeworth and developed subsequently by Karl Pearson and Udny Yule, were seized upon and transformed by Ronald Fisher and Jerzy Neyman and Egon S. -
Bayesian Data-Analysis Toolbox User Manual
Bayesian Data-Analysis Toolbox Release 4.23, Manual Version 3 G. Larry Bretthorst Biomedical MR Laboratory Washington University School Of Medicine, Campus Box 8227 Room 2313, East Bldg., 4525 Scott Ave. St. Louis MO 63110 http://bayes.wustl.edu Email: [email protected] September 18, 2018 Appendix C Thermodynamic Integration Thermodynamic Integration is a technique used in Bayesian probability theory to compute the posterior probability for a model. As a reminder, if a set of m models is designated as M 2 f1; 2; : : : ; mg, then one can compute the posterior probability for the models by an application of Bayes' Theorem [1] P (MjDI) / P (MjI)P (DjMI) (C.1) where we have dropped a normalization constant, M = 1 means we are computing the posterior probability for model 1, M = 2 the probability for model 2, etc. The three terms in this equation, going from left to right, are the posterior probability for the model indicator given the data and the prior information, P (MjDI), the prior probability for the model given only the prior information, P (MjI), and the marginal direct probability for the data given the model and the prior information, P (DjMI). The marginal direct probability for the data given a model can be computed from the joint posterior probability for the data and the model parameters, which we will call Ω, given the Model and the prior information Z P (DjMI) = dΩP (ΩjMI)P (DjΩMI): (C.2) Unfortunately, the integrals on the right-hand side of this equation can be very high dimensional. Consequently, although we know exactly what calculation must be done to compute the marginal direct probability, in most applications the integrals are not tractable. -
Top-25 Publications
David Leigh (University of Manchester, UK) – 25 most significant publications (reverse chronological order) 1. "A catalysis-driven artificial molecular pump", S. Amano, S. D. P. Fielden and D. A. Leigh, Nature 594, 529-534 (2021). 2. "A molecular endless (74) knot", D. A. Leigh, J. J. Danon, S. D. P. Fielden, J.-F. Lemonnier, G. F. S. Whitehead and S. L. Woltering, Nat. Chem. 13, 117-122 (2021). 3. "Self-assembly of a layered 2D molecularly woven fabric", D. P. August, R. A. W. Dryfe, S. J. Haigh, P. R. C. Kent, D. A. Leigh, J.-F. Lemonnier, Z. Li, C. A. Muryn, L. I. Palmer, Y. Song, G. F. S. Whitehead and R. J. Young, Nature 588, 429-435 (2020). 4. "Tying different knots in a molecular strand", D. A. Leigh, F. Schaufelberger, L. Pirvu, J. Halldin Stenlid, D. P. August and J. Segard, Nature 584, 562-568 (2020). 5. "Rotary and linear molecular motors driven by pulses of a chemical fuel", S. Erbas-Cakmak, S. D. P. Fielden, U. Karaca, D. A. Leigh, C. T. McTernan, D. J. Tetlow and M. R. Wilson, Science 358, 340-343 (2017). Cited 154 times as of 23.6.21 (GooSch). 6. "Stereodivergent synthesis with a programmable molecular machine", S. Kassem, A. T. L. Lee, D. A. Leigh, V. Marcos, L. I. Palmer and S. Pisano, Nature 549, 374-378 (2017). Cited 113 times as of 23.6.21 (GooSch). 7. "Braiding a molecular knot with eight crossings", J. J. Danon, A. Krüger, D. A. Leigh, J.-F. Lemonnier, A. J. Stephens, I. -
The Magic of Chemistry David Leigh Has a Love of Chemistry...And Magic
Chemical Science Interview The magic of chemistry David Leigh has a love of chemistry...and magic. Alison Stoddart finds out more Who inspired you to become a scientist? You like to practice magic. What is the most magical My high school chemistry teacher Dave Clarke. I thing about chemistry to you? think that’s a common thing amongst chemists. Chemistry allows you create things that never He made chemistry seem fun and exciting. There were and to change the world and change society, were several others from my class who went on and that’s an amazing thing. James Clerk Maxwell, to do chemistry. Barry Moore, on the chemistry the 19th century Scottish physicist, came up with faculty at Strathclyde, also went to my high school. the theory that light was really electromagnetic radiation. A feat of which Richard Feynman said, What motivated you to study molecular machines? ‘…ten thousand years from now - there can be little I worked in Fraser Stoddart’s group before he doubt that the most significant event of the 19th made any catenanes or rotaxanes. We made our century will be judged as Maxwell’s discovery of first catenane about five years after Fraser did. I the laws of electrodynamics.’ I think there are still could see that there was nothing interesting about those kinds of things to be discovered and there is a catenane or a rotaxane in itself. The interesting still that impact to be had on society. thing about them is the possibility to control a well-defined, large-amplitude motion. That’s How long have you been a magician? what you need to make a molecular machine.