Filed under: Books, Kids, Statistics, University life ]]>

Filed under: Books, Statistics, University life Tagged: impact factor, John Wiley, JRSSB, Series B ]]>

Filed under: Books, Kids, Statistics Tagged: Amazon, bestseller, book review, FiveThirtyEight, Guesstimation, Nate Silver, what if?, xkcd ]]>

“Paved roads had long ago surrended to gravel tracks that disappeared into a desert of snow covered lava. Black spires like a forest of charred trees blotted out the stars near the horizon.”

**T**his is the last book I read from my Amazon package: *Available Dark* by Elizabeth Hand. I cannot remember how I came to order it… Maybe a confusion with another fantasy author like Elizabeth Moon? Or simply because the story was taking place between Maine, Finland and Iceland?! Anyway, I read the book within two days during a short hiking trip to the volcano region of Central France. The plot has indeed a mesmerizing quality that made me keep reading further and further at ungodly hours. (With the help of an US jetlag.) It is original and intense enough to overcome the major difficulty that the central character, Cas, is far from sympathetic, from specialising in corpse photography to being almost constantly on drugs. But the construction of the plot and the introduction of the characters, always seen from Cas’ viewpoint, are well-done, even though the ending is both precipitated and unrealistic. Too many coincidences. The original setup of this novel is the Finnish black metal scene, with its undercurrents of satanism, ritual murders, and church burnings. Rather accurate judging from the wikipedia page on the topic! What I appreciated most was the description of the first impression of Iceland on Cas, when she landed from Helsinki. *“The trip to Reykjavik [from the airport] was like a bus tour through Mordor. Black lava fields, an endless waste broken here and there by ruined machinery or a building of stained corrugated metal.”* So I may consider reading another novel in the series in a near future…

Filed under: Books, Mountains, Travel Tagged: Available Dark, black metal, Elizabeth Hand, Finland, Helsinki, Iceland, Reykjavik ]]>

and the most frequent search terms (excluding those connected with my name), with again two beach towns at the top!

benidorm | 1,804 |

surfers paradise | 1,050 |

george casella | 785 |

mont blanc | 705 |

introducing monte carlo methods with r | 587 |

marie curie | 500 |

mistborn | 480 |

millenium | 413 |

i love r | 411 |

andrew wyeth | 398 |

abele blanc | 385 |

bayesian p value | 375 |

bayesian p-value | 374 |

walter bonatti | 351 |

nested sampling | 333 |

particle mcmc | 332 |

dumplings | 298 |

Filed under: Books, Kids, Statistics Tagged: book reviews, guest post ]]>

Mixtures of distributions are fascinating objects for statisticians in that they both constitute a straightforward extension of standard distributions and offer a complex benchmark for evaluating statistical procedures, with a likelihood both computable in a linear time and enjoying an exponential number of local models (and sometimes infinite modes). This fruitful playground appeals in particular to Bayesians as it constitutes an easily understood challenge to the use of improper priors and of objective Bayes solutions. This talk will review some ancient and some more recent works of mine on mixtures of distributions, from the 1990 Gibbs sampler to the 2000 label switching and to later studies of Bayes factor approximations, nested sampling performances, improper priors, improved importance samplers, ABC, and a inverse perspective on the Bayesian approach to testing of hypotheses.

**I** am very grateful to the scientific committee for this invitation, as it will give me the opportunity to meet the new generation, learn from them and in addition discover Vienna where I have never been, despite several visits to Austria. Including its top, the Gro*ß*glockner. I will also give a seminar in Linz the day before. In the Institut für Angewandte Statistik.

Filed under: Books, Kids, Mountains, pictures, Statistics, Travel, University life Tagged: Austria, Bayes factor, Bayesian tests of hypotheses, BAYSM, Gibbs sampling, importance sampling, label switching, Linz, mixtures, Vienna, WU Wirtschaftsuniversität Wien ]]>

Filed under: pictures, Statistics, Travel, University life Tagged: José Miguel Bernardo, O-Bayes 2015, objective Bayes, Spain, Susie Bayarri, Valencia conferences ]]>

Filed under: pictures, Statistics, Travel Tagged: elections, Glasgow, Hillhead, independence, Nate Silver, poll, Scotland, Scottish independence referendum, United Kingdom ]]>

*“The term *psi* denotes anomalous processes of information or energy transfer that are currently unexplained in terms of known physical or biological mechanisms.”*

**W**hen re-reading [in the taxi to Birmingham airport] Bem’s piece on “significant” ESP tests, I came upon the following hilarious part that I could not let pass:

“For most psychological experiments, a random number table or the random function built into most programming languages provides an adequate tool for randomly assigning participants to conditions or sequencing stimulus presentations. For both methodological and conceptual reasons, however, psi researchers have paid much closer attention to issues of randomization.

At the methodological level, the problem is that the random functions included in most computer languages are not very good in that they fail one or more of the mathematical tests used to assess the randomness of a sequence of numbers (L’Ecuyer, 2001), such as Marsaglia’s rigorous Diehard Battery of Tests of Randomness (1995). Such random functions are sometimes called pseudo random number generators (PRNGs) because they [are] not random in the sense of being indeterminate because once the initial starting number (the seed) is set, all future numbers in the sequence are fully determined.”

**W**ell, pseudo-random generators included in all modern computer languages that I know have passed tests like diehard. It would be immensely useful to learn of counterexamples as those using the corresponding language should be warned!!!

“In contrast, a hardware-based or “true” RNG is based on a physical process, such as radioactive decay or diode noise, and the sequence of numbers is indeterminate in the quantum mechanical sense. This does not in itself guarantee that the resulting sequence of numbers can pass all the mathematical tests of randomness (…) Both Marsaglia’s own PRNG algorithm and the “true” hardware-based Araneus Alea I RNG used in our experiments pass all his diehard tests (…) At the conceptual level, the choice of a PRNG or a hardware-based RNG bears on the interpretation of positive findings. In the present context, it bears on my claim that the experiments reported in this article provide evidence for precognition or retroactive influence.”

**T**here is no [probabilistic] validity in the claim that hardware random generators are more random than pseudo-random ones. Hardware generators may be unpredictable even by the hardware conceptor, but the only way to check they produce generations from a uniform distribution follows exactly the same pattern as for PRNG. And the lack of reproducibility of the outcome makes it impossible to check the reproducibility of the study. But here comes the best part of the story!

“If an algorithm-based PRNG is used to determine the successive left-right positions of the target pictures, then the computer already “knows” the upcoming random number before the participant makes his or her response; in fact, once the initial seed number is generated, the computer implicitly knows the entire sequence of left/right positions. As a result, this information is potentially available to the participant through real-time clairvoyance, permitting us to reject the more extraordinary claim that the direction of the causal arrow has actually been reversed.”

**E**xtraordinary indeed… But not more extraordinary than conceiving that a [psychic] participant in the experiment may “see” the whole sequence of random numbers!

“In contrast, if a true hardware-based RNG is used to determine the left/right positions, the next number in the sequence is indeterminate until it is actually generated by the quantum physical process embedded in the RNG, thereby ruling out the clairvoyance alternative. This argues for using a true RNG to demonstrate precognition or retroactive influence. But alas, the use of a true RNG opens the door to the psychokinesis interpretation: The participant might be influencing the placement of the upcoming target rather than perceiving it, a possibility supported by a body of empirical evidence testing psychokinesis with true RNGs (Radin, 2006, pp.154–160).”

**G**ood! I was just about to make the very same objection! If someone can predict the whole sequence of [extremely long integer] values of a PRNG, it gets hardly any more irrational to imagine that he or she can mentally impact a quantum mechanics event. (And hopefully save Schröninger’s cat in the process.) Obviously, it begs the question as to how a subject could forecast a location of the picture that depends on the random generation but not forecast the result of the random generation.

“Like the clairvoyance interpretation, the psychokinesis interpretation also permits us to reject the claim that the direction of the causal arrow has been reversed. Ironically, the psychokinesis alternative can be ruled out by using a PRNG, which is immune to psychokinesis because the sequence of numbers is fully determined and can even be checked after the fact to confirm that its algorithm has not been perturbed. Over the course of our research program—and within the experiment just reported—we have obtained positive results using both PRNGs and a true RNG, arguably leaving precognition/reversed causality the only nonartifactual interpretation that can account for all the positive results.”

**T**his is getting rather confusing. Avoid using a PRNG for fear the subject infers about the sequence and avoid using a RNG for fear of the subject tempering with the physical generator. An omniscient psychic would be able to hand both types of generators, wouldn’t he or she!?!

“This still leaves open the artifactual alternative that the output from the RNG is producing inadequately randomized sequences containing patterns that fortuitously match participants’ response biases.”

**T**his objection shows how little confidence the author has in the randomness tests he previously mentioned: a proper random generator is not *inadequately* *randomized*. And if chance only rather than psychic powers is involved, there is no explanation for the match with the participants’ response. Unless those participants are so clever as to detect the flaws in the generator…

“In the present experiment, this possibility is ruled out by the twin findings that erotic targets were detected significantly more frequently than randomly interspersed nonerotic targets and that the nonerotic targets themselves were not detected significantly more frequently than chance. Nevertheless, for some of the other experiments reported in this article, it would be useful to have more general assurance that there are not patterns in the left/right placements of the targets that might correlate with response biases of participants. For this purpose, Lise Wallach, Professor of Psychology at Duke University, suggested that I run a virtual control experiment using random inputs in place of human participants.”

**A**bsolutely brilliant! This test replacing the participants with random generators has shown that the subjects’ answers do not correspond to an iid sequence from a uniform distribution. It would indeed require great psychic powers to reproduce a perfectly iid U(0,1) sequence! And the participants were warned about the experiment so naturally expected to see patterns in the sequence of placements.

Filed under: Books, Statistics Tagged: Birmingham, DieHard, ESP, hardware random generator, PRNG, pseudo-random generator, random simulation, randomness ]]>

NIPS 2014 Workshop: ABC in Montreal

**December 12, 2014**

** Montréal, Québec, Canada**

Approximate Bayesian computation (ABC) or likelihood-free (LF) methods have developed mostly beyond the radar of the machine learning community, but are important tools for a large segment of the scientific community. This is particularly true for systems and population biology, computational psychology, computational chemistry, etc. Recent work has both applied machine learning models and algorithms to general ABC inference (NN, forests, GPs) and ABC inference to machine learning (e.g. using computer graphics to solve computer vision using ABC). In general, however, there is significant room for collaboration between the two communities.

The workshop will consist of invited and contributed talks, poster spotlights, and a poster session. Rather than a panel discussion we will encourage open discussion between the speakers and the audience!

Examples of topics of interest in the workshop include (but are not limited to):

* Applications of ABC to machine learning, e.g., computer vision, inverse problems

* ABC in Systems Biology, Computational Science, etc

* ABC Reinforcement Learning

* Machine learning simulator models, e.g., NN models of simulation responses, GPs etc.

* Selection of sufficient statistics

* Online and post-hoc error

* ABC with very expensive simulations and acceleration methods (surrogate modeling, choice of design/simulation points)

* ABC with probabilistic programming

* Posterior evaluation of scientific problems/interaction with scientists

* Post-computational error assessment

* Impact on resulting ABC inference

* ABC for model selection

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**Submission:**

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We invite submissions in NIPS 2014 format with a maximum of 4 pages, excluding references. Anonymity is not required. Relevant works that have been recently published or presented elsewhere are allowed, provided that previous publications are explicitly acknowledged. Please submit papers in PDF format to abcinmontreal@gmail.com .

===============

**ISBA@NIPS**

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This workshop has been endorsed by ISBA. As part of their sponsorship, ISBA will be awarding a limited number of travel awards to PhD students and young researchers. The organizing committee may nominate particularly strong submissions for this award.

In addition to the general ISBA endorsement, ABC in Montréal has been endorsed by the BayesComp section of ISBA.

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**Important Dates:**

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Submission Deadline: October 9, 2014

Author Notification: October 26, 2014

Workshop: December 12 or 13, 2014

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**Invited Speakers:**

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Michael Blum, Laboratoire TIMC-IMAG, Grenoble

Juliane Liepe, Imperial College London

Vikash Mansinghka, MIT

Frank Wood, Oxford

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**Organizers:**

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Neil Lawrence, University of Sheffield

Ted Meeds, University of Amsterdam

Christian Robert, Université Paris-Dauphine

Max Welling, University of Amsterdam

Richard Wilkinson, University of Nottingham

**Contact:**

The organizers can be contacted at abcinmontreal@gmail.com.

Filed under: Statistics, Travel, University life Tagged: ABC, BayesComp, Canada, ISBA@NIPS, likelihood-free methods, machine learning, Montréal, NIPS 2014, Québec, simulation ]]>