[ccp4bb] CCP4 Study Weekend 2022

[Karen McIntyre - STFC UKRI]

Dear all,

Registration is open for the 2022 CCP4 study weekend https://cvent.me/YPqVd0

The topic this year is "Current trends in macromolecular model refinement and 
validation".

This year the meeting will be a hybrid meeting from 5th - 7th January 2022 with 
the in-person event held at East Midlands Conference Centre, Nottingham.

Due to Covid we have had to make a few changes to the event.  We are limiting 
in-person attendance to UK-based delegates only and we only have 220 spaces for 
in-person delegates due to social distancing - overseas delegates (and any 
UK-based delegates that either cannot get an in-person space or do not wish to 
attend in-person) may attend the meeting virtually and we have an unlimited 
number of virtual spaces.

A preliminary programme, registration details, and further information can be 
found at the above link.

Key Dates

  *   Early bird registration final date: 14 November 2021
  *   Standard Student bursaries are open to all students registering during 
early bird period i.e. now until 14 November.

  *   Registration closes 6 December 2021 (in-person when places run out)

We hope to see you there!

The Scientific Organisers
Rob Nicholls, Svetlana Antonyuk and Melanie Vollmar.

Administrative Organiser
Karen McIntryre

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Re: [ccp4bb] am I doing this right?

[Gergely Katona]

Hi,

I have more estimates to the same problem using a multinomial data 
distribution. I should have realized that for prediction, I do not have to deal 
with infinite likelihood of 0 trials when observing only 0s on an image. 
Whenever 0 photons generated by the latent process, the image is automatically 
empty. With this simplification, I still have to hide behind mathematical 
convenience and use Gamma prior for the latent Poisson process, but observing 0 
counts just increments the beta parameter by 1 compared to the prior belief. 
With equal photon capture probabilities, the mean counts are about 0.01 and the 
std is about 0.1 with rate≈Gamma(alpha=1, beta=0.1) prior . With a symmetric 
Dirichlet prior to the capture probabilities, the means appear unchanged, but 
the predicted stds starts high at very low concentration parameter and level 
off at high concentration parameter. This becomes more apparent at high photon 
counts (high alpha of Gamma distribution). The answer is different if we look 
at the std across the detector plane or across time of a single pixel.
Details of the calculation below:

https://colab.research.google.com/drive/1NK43_3r1rH5lBTDS2rzIFDFNWqFfekrZ?usp=sharing

Best wishes,

Gergely

Gergely Katona, Professor, Chairman of the Chemistry Program Council
Department of Chemistry and Molecular Biology, University of Gothenburg
Box 462, 40530 Göteborg, Sweden
Tel: +46-31-786-3959 / M: +46-70-912-3309 / Fax: +46-31-786-3910
Web: http://katonalab.eu, Email: gergely.kat...@gu.se

From: CCP4 bulletin board  On Behalf Of Nave, Colin 
(DLSLtd,RAL,LSCI)
Sent: 21 October, 2021 19:21
To: CCP4BB@JISCMAIL.AC.UK
Subject: Re: [ccp4bb] am I doing this right?

Congratulations to James for starting this interesting discussion.

For those who are like me, nowhere near a black belt in statistics, the thread 
has included a number of distributions.  I have had to look up where these 
apply and investigate their properties.
As an example,
“The Poisson distribution is used to model the # of events in the future, 
Exponential distribution is used to predict the wait time until the very first 
event, and Gamma distribution is used to predict the wait time until the k-th 
event.”
A useful calculator for distributions can be found at
https://keisan.casio.com/menu/system/0540
a specific example is at
https://keisan.casio.com/exec/system/1180573179
where cumulative probabilities for a Poisson distribution can be found given 
values for x and lambda.

The most appropriate prior is another issue which has come up e.g. is a flat 
prior appropriate? I can see that a different prior would be appropriate for 
different areas of the detector (e.g. 1 pixel instead of 100 pixels) but the 
most appropriate prior seems a bit arbitrary to me. One of James’ examples was 
10^5 background photons distributed among  10^6 pixels – what is the most 
appropriate prior for this case? I presume it is OK to update the prior after 
each observation but I understand that it can create difficulties if not done 
properly.

Being able to select the prior is sometimes seen as a strength of Bayesian 
methods. However, as a strong advocate of Bayesian methods once put it, this is 
a bit like Achilles boasting about his heel!

I hope for some agreement among the black belts. It would be good to end up 
with some clarity about the most appropriate probability distributions and 
priors. Also, have we got clarity about the question being asked?

Thanks to all for the interesting points.

Colin
From: CCP4 bulletin board mailto:CCP4BB@JISCMAIL.AC.UK>> 
On Behalf Of Randy John Read
Sent: 21 October 2021 13:23
To: CCP4BB@JISCMAIL.AC.UK
Subject: Re: [ccp4bb] am I doing this right?

Hi Kay,

No, I still think the answer should come out the same if you have good reason 
to believe that all the 100 pixels are equally likely to receive a photon (for 
instance because your knowledge of the geometry of the source and the detector 
says the difference in their positions is insignificant, i.e. part of your 
prior expectation). Unless the exact position of the spot where you detect the 
photon is relevant, detecting 1 photon on a big pixel and detecting the same 
photon on 1 of 100 smaller pixels covering the same area are equivalent events. 
What should be different in the analysis, if you're thinking about individual 
pixels, is that the expected value for a photon landing on any of the pixels 
will be 100 times lower for each of the smaller pixels than the single big 
pixel, so that the expected value of their sum is the same. You won't get to 
that conclusion without having a different prior probability for the two cases 
that reflects the 100-fold lower flux through the smaller area, regardless of 
the total power of the source.

Best wishes,
Randy

On 21 Oct 2021, at 13:03, Kay Diederichs 
mailto:kay.diederi...@uni-konstanz.de>> wrote:

Randy,

I must admit that I am not certain about my answer, but I lean