Archive for San Francisco

Altered Carbon [season 1]

Posted in Books, pictures with tags , , , , , , on April 12, 2020 by xi'an

Following my reading of the rather thin (plot-wise) Thin Air, I took advantage of the virus to watch Netflix Altered Carbon. Which is based, roughly, on Richard Morgan’s book. While I enjoyed watching the efficient series, I failed to see a deeper message beyond the cyberpunk detective story, message that was indeed in the book. The show is very efficient with a well rendered futuristic San Francisco. Reminding me of Blade Runner, obviously. But also of the novels of William Gibson in many ways. Including this transformation of the Golden Gate Bridge into a container community. And the somewhat anachronistic fascination for samurais and yakuzas. A choice leading to repeated (wo)man to (wo)man fights that tend to become repetitive, a fairly high level of cruelty, sadism, gory and graphical episodes, definitely not a family show!, another futuristic and bleaker version of Chandler’s Farewell my Lovely, with the special twist of the murdered investigating his own murder already at the core of the book. But the lack of a deeper political message dilutes the appeal and somewhat the tension of the show, making somehow the existence of characters with a conscience hard to believe. A plus for the AI turned Edgar Poe turned The Raven hotel though! And a minus for the “happy ending.”..

unmistakable hints of being in the US

Posted in pictures, Travel with tags , , , , , , , , on August 27, 2016 by xi'an

ABC by subset simulation

Posted in Books, Statistics, Travel with tags , , , , , , , , , on August 25, 2016 by xi'an

Last week, Vakilzadeh, Beck and Abrahamsson arXived a paper entitled “Using Approximate Bayesian Computation by Subset Simulation for Efficient Posterior Assessment of Dynamic State-Space Model Classes”. It follows an earlier paper by Beck and co-authors on ABC by subset simulation, paper that I did not read. The model of interest is a hidden Markov model with continuous components and covariates (input), e.g. a stochastic volatility model. There is however a catch in the definition of the model, namely that the observable part of the HMM includes an extra measurement error term linked with the tolerance level of the ABC algorithm. Error term that is dependent across time, the vector of errors being within a ball of radius ε. This reminds me of noisy ABC, obviously (and as acknowledged by the authors), but also of some ABC developments of Ajay Jasra and co-authors. Indeed, as in those papers, Vakilzadeh et al. use the raw data sequence to compute their tolerance neighbourhoods, which obviously bypasses the selection of a summary statistic [vector] but also may drown signal under noise for long enough series.

“In this study, we show that formulating a dynamical system as a general hierarchical state-space model enables us to independently estimate the model evidence for each model class.”

Subset simulation is a nested technique that produces a sequence of nested balls (and related tolerances) such that the conditional probability to be in the next ball given the previous one remains large enough. Requiring a new round of simulation each time. This is somewhat reminding me of nested sampling, even though the two methods differ. For subset simulation, estimating the level probabilities means that there also exists a converging (and even unbiased!) estimator for the evidence associated with different tolerance levels. Which is not a particularly natural object unless one wants to turn it into a tolerance selection principle, which would be quite a novel perspective. But not one adopted in the paper, seemingly. Given that the application section truly compares models I must have missed something there. (Blame the long flight from San Francisco to Sydney!) Interestingly, the different models as in Table 4 relate to different tolerance levels, which may be an hindrance for the overall validation of the method.

I find the subsequent part on getting rid of uncertain prediction error model parameters of lesser [personal] interest as it essentially replaces the marginal posterior on the parameters of interest by a BIC approximation, with the unsurprising conclusion that “the prior distribution of the nuisance parameter cancels out”.

off to Australia

Posted in pictures, Statistics, Travel, University life, Wines with tags , , , , , , , , , on August 22, 2016 by xi'an

south bank of the Yarra river, Melbourne, July 21, 2012Taking advantage of being in San Francisco, I flew yesterday to Australia over the Pacific, crossing for the first time the day line. The 15 hour Qantas flight to Sydney was remarkably smooth and quiet, with most passengers sleeping for most of the way, and it gave me a great opportunity to go over several papers I wanted to read and review. Over the next week or so, I will work with my friends and co-authors David Frazier and Gael Martin at Monash University (and undoubtedly enjoy the great food and wine scene!). Before flying back to Paris (alas via San Francisco rather than direct).

San Francisco [escape route]

Posted in Kids, pictures, Running, Travel with tags , , , , on August 8, 2016 by xi'an