Sam Power
@spmontecarlo.bsky.social
Lecturer in Maths & Stats at Bristol. Interested in probabilistic + numerical computation, statistical modelling + inference. (he / him).
Homepage: https://sites.google.com/view/sp-monte-carlo
Seminar: https://sites.google.com/view/monte-carlo-semina
Homepage: https://sites.google.com/view/sp-monte-carlo
Seminar: https://sites.google.com/view/monte-carlo-semina
another day volunteering at the topology museum.
November 27, 2025 at 3:24 PM
another day volunteering at the topology museum.
This project was initiated during our residence at a 2024 INI programme on "Stochastic Systems for Anomalous Diffusion" (www.newton.ac.uk/event/ssd/), wherein many presented works treated specific approaches to the 'robustness' issue. It seemed an opportune time to take stock of things.
November 27, 2025 at 10:35 AM
This project was initiated during our residence at a 2024 INI programme on "Stochastic Systems for Anomalous Diffusion" (www.newton.ac.uk/event/ssd/), wherein many presented works treated specific approaches to the 'robustness' issue. It seemed an opportune time to take stock of things.
With Giorgos Vasdekis, we have written a manuscript - arxiv.org/abs/2511.21563 - which surveys the state of affairs within this literature, outlining signals for anticipating non-robustness, principles for improving robustness, and examples of contemporary methods which confront these issues.
November 27, 2025 at 10:35 AM
With Giorgos Vasdekis, we have written a manuscript - arxiv.org/abs/2511.21563 - which surveys the state of affairs within this literature, outlining signals for anticipating non-robustness, principles for improving robustness, and examples of contemporary methods which confront these issues.
In response to this, there have been a range of proposed MCMC strategies which aim to
i) perform acceptably when these conditions hold, but
ii) degrade gracefully when these conditions start to break down,
collectively giving rise to a burgeoning literature on 'robust MCMC'.
i) perform acceptably when these conditions hold, but
ii) degrade gracefully when these conditions start to break down,
collectively giving rise to a burgeoning literature on 'robust MCMC'.
November 27, 2025 at 10:35 AM
In response to this, there have been a range of proposed MCMC strategies which aim to
i) perform acceptably when these conditions hold, but
ii) degrade gracefully when these conditions start to break down,
collectively giving rise to a burgeoning literature on 'robust MCMC'.
i) perform acceptably when these conditions hold, but
ii) degrade gracefully when these conditions start to break down,
collectively giving rise to a burgeoning literature on 'robust MCMC'.
humans in the 1960s / potentially slightly earlier
November 25, 2025 at 10:09 AM
humans in the 1960s / potentially slightly earlier
(The pictured set is conjecturally optimal for the problem described)
November 24, 2025 at 1:29 PM
(The pictured set is conjecturally optimal for the problem described)
I'm also slightly reminded of the Beta function, but without any particular conclusions for now.
November 21, 2025 at 6:04 PM
I'm also slightly reminded of the Beta function, but without any particular conclusions for now.
I like it! It hadn't occurred to me that it is somehow 'really' a product of two (rather than three) factors, but I think that I'm on board. A fan of falling / rising factorials, in any case.
November 21, 2025 at 6:00 PM
I like it! It hadn't occurred to me that it is somehow 'really' a product of two (rather than three) factors, but I think that I'm on board. A fan of falling / rising factorials, in any case.
I read the caption first and thought the illusion might be because the y-axis was on a log scale, but it's not!
My brain was bamboozled regardless.
My brain was bamboozled regardless.
November 21, 2025 at 4:47 PM
I think this - arxiv.org/abs/2006.13700 - is a good starting point, IIRC.
Inference in Stochastic Epidemic Models via Multinomial Approximations
We introduce a new method for inference in stochastic epidemic models which uses recursive multinomial approximations to integrate over unobserved variables and thus circumvent likelihood intractabili...
arxiv.org
November 19, 2025 at 7:45 PM
I think this - arxiv.org/abs/2006.13700 - is a good starting point, IIRC.
Very nice! Some of the latter part seemed natural to think of in terms of Markov kernels / channels. A friend had some recent work on epidemiological models where this conjugacy between Poisson / Multinomial and 'colouring' channels was quite computationally useful.
November 19, 2025 at 12:02 PM
Very nice! Some of the latter part seemed natural to think of in terms of Markov kernels / channels. A friend had some recent work on epidemiological models where this conjugacy between Poisson / Multinomial and 'colouring' channels was quite computationally useful.
Prompted by glancing at arxiv.org/abs/2511.14200.
November 19, 2025 at 10:18 AM
Prompted by glancing at arxiv.org/abs/2511.14200.
One application of this would be to compare the features of different biased random walks, since the convex ordering is preserved under independent summation, and allows for the control of { variance, large deviations, etc. }.
November 19, 2025 at 10:16 AM
One application of this would be to compare the features of different biased random walks, since the convex ordering is preserved under independent summation, and allows for the control of { variance, large deviations, etc. }.
However, this changes if you allow for a dilation factor. In particular, there is always some σ = σ(p, q) such that Z(p) is dominated by σ⋅Z(q). After a bit of book-keeping and changing variables for convenience, one can deduce the attached formula.
November 19, 2025 at 10:16 AM
However, this changes if you allow for a dilation factor. In particular, there is always some σ = σ(p, q) such that Z(p) is dominated by σ⋅Z(q). After a bit of book-keeping and changing variables for convenience, one can deduce the attached formula.
I do get a kick out of 'punctual processes'!
November 18, 2025 at 5:26 PM
I do get a kick out of 'punctual processes'!