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step-dads with Bayesian design [One World ABC’minar, 21 March]

Posted in Books, Statistics, University life with tags , , , , , , , , , , , , , on March 18, 2024 by xi'an

The next One World ABC seminar is taking place (on-line, requiring pre-registration) on Thursday 21 March, 9:00am UK time, with Desi Ivanova (University of Oxford), speaking about Step-DAD: Semi-Amortized Policy-Based Bayesian Experimental Design:

We develop a semi-amortized, policy-based, approach to Bayesian experimental design (BED) called Step-wise Deep Adaptive Design (Step-DAD). Like existing, fully amortized, policy-based BED approaches, Step-DAD trains a design policy upfront before the experiment. However, rather than keeping this policy fixed, Step-DAD periodically updates it as data is gathered, refining it to the particular experimental instance. This allows it to improve both the adaptability and the robustness of the design strategy compared with existing approaches.

(Which reminded me of George’s book on design in 2008.)

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practical PDMP

Posted in Books, Statistics, University life with tags , , , , , , , , , , , on December 9, 2021 by xi'an

While in Warwick, last month, I attended a reading group on PDMPs where Filippo Pagani talked about practical PDMP, connected with a recent arXival by Bertazzi, Bierkens and Dobson. The central question when implementing PDMP is to find a realistic way of solving

\int_0^\tau \lambda(x+tv,v)\text dt = \epsilon\quad\epsilon\sim\mathcal Exp(1)

to decide on the stopping time (when the process ceases to be deterministic). The usual approach is to use Poisson thinning by finding an upper bound on λ, but this is either difficult or potentially inefficient (and sometimes both).

“finding a sharp bound M(s) [for Poisson thinning] can be an extremely challenging problem in most practical settings (…) In order to overcome this problem, we introduce discretisation schemes for PDMPs which make their
approximate simulation possible.”

Some of the solutions proposed in Bertazzi et al. are relying on

  1. using a frozen (fixed) λ
  2. discretising time and the integral (first order scheme)
  3. allowing for more than a jump over a time interval (higher order schemes)
  4. going through control variates (when gradient is Lipschitz and Hessian bounded, with known constants) as it produces a linear rate λ
  5. subsampling (at least for Zig Zag)

with theoretical guarantees that the approximations are convergent, as the time step goes to zero. They (almost obviously) remain model dependent solutions (with illustrations for the Zig Zag and bouncy particle versions), with little worse case scenarios, but this is an extended investigation into making PDMPs more manageable!

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Comox [K’òmoks] snapshot [jatp]

Posted in Statistics with tags , , , , , , , on August 23, 2018 by xi'an

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