Bayes@Lund 2019

Session

Bayes@Lund 2019

7 May 2019, 09:05

Palaestra, Lund University, Sweden

23. Hierarchical models and their applications in astronomy

Maggie Lieu (ESA)

07/05/2019, 09:05

Bayes@Lund 2019 Meeting

Maggie is an astrophysics research fellow working at the European Space Agency in Madrid. Her main research involves modelling the mass distribution of clusters of galaxies to understand the nature of dark matter and dark energy in our Universe. Maggie will be talking about how hierarchical models can be a powerful tool for inference.

4. What cause successful learning in Bayesian methods?

George Moroz (National Research University Higher School of Economics)

07/05/2019, 10:00

Bayes@Lund 2019 Meeting

Contributed talk

I would like to share the comparison of three groups of students that I taught Bayesian methods this year:

• mixed group (psychology, biology...) with good background in R and frequentist statistics;
• linguistic group (3d year) with medium background in R and frequentist statistics;
• further education group in Computer Linguistic with beginner R and no statistical background.

I expected...

20. Introducing Bayesian Stats through Signal Detection Theory

Gerit Pfuhl (UiT)

07/05/2019, 10:20

Bayes@Lund 2019 Meeting

Contributed talk

To avoid starting with a formula a detour via utilized Signal detection theory (uSDT) familiarizes psychology undergraduates with some of the basic concepts in Bayesian statistics. uSDT includes payoffs (utility functions), base rates, and varies similarities, illustrated on perceptual decision processes. Payoffs and base rates influences bias, assisting students in understanding models and...

19. Prior thoughts on mixed-membership models in linguistics

Chundra Cathcart

07/05/2019, 11:00

Bayes@Lund 2019 Meeting

Contributed talk

Bayesian mixed-membership models are popular in linguistics, as they explicitly model contact between languages (Reesink et al 2009, Syrjänen et al 2016). Most linguistic applications use the biological Structure program (Pritchard et al 2000) with default presets, fixing the concentration parameter of the population-level Dirichlet prior over allele frequency (treated as an analog for the...

6. A Bayesian method to localize lost gamma sources

Antanas Bukartas (Lund University)

07/05/2019, 11:20

Bayes@Lund 2019 Meeting

Contributed talk

Radioactive sources can sometimes be lost or misplaced despite the existing rigorous safety rules. Lost sources must be found as soon as possible to avoid inflicting harm to the public. Regardless of the type of equipment used it is desirable to use as much information as possible from the measurements to draw conclusions about the activity and location of a detected source. Using Bayesian...

13. Spatio-Temporal Reconstructions of Global CO2-Fluxes using Gaussian Markov Random Fields

Unn Dahlén

07/05/2019, 11:40

Bayes@Lund 2019 Meeting

Contributed talk

Atmosperic invere modelling is a method for constraining Earth surface fluxes (sinks and sources) of green house gases using measurements of athmosperic concentrations. The (linear) link between atmospheric concentration and fluxes are provided by an atmospheric transport model. Since the number of unknown surfaces fluxes is much larger than the number of observed atmospheric concentrations,...

25. Visualisation for refining and communicating Bayesian analyses

Robert Grant (BayesCamp)

07/05/2019, 12:45

Introductory statistics classes teach the value of basic plots to diagnose problems with regression models. Like most of us, I found these very boring and spent a long time avoiding them. Now that I focus on complex Bayesian models, I find visualisation of variables, predictions and residuals more useful than ever. Analysts need to track down problems where the model is not fitting the data...

14. Bayesian vs. Frequentism for experimentalists

Jakob Lavröd (IYPT Sweden)

07/05/2019, 13:30

Bayes@Lund 2019 Meeting

Contributed talk

A common rebuttal to Bayesian methods is that they are appropriate for large and complex problems (containing prior information and hidden variables), and most undergraduate teaching is often based upon frequentist methods. Starting from the context of the practical experimentalist, we explore the difference between the Bayesian and Frequentist methodology and highlights the advantages of...

8. Extending Bayes to Make Optimal Decisions

Jonas Kristoffer Lindeløv (Department of Communication and Psychology, Aalborg University, Denmark)

07/05/2019, 14:00

Bayes@Lund 2019 Tutorials

Short tutorial

Utility Theory allow you to make optimal decisions in the face of uncertainty. For example, what bidding price would maximize your earnings, taking the chance of failure into account? Utility Theory latches nicely onto Bayesian Inference. Once you have a posterior distribution, you need only a few more lines of code to apply a utility function (aka loss function) and identify the decision that...

5. Rich-man's Monte Carlo: Uncertainty Analysis in Excel

Dmytro Perepolkin (Lund University)

07/05/2019, 14:20

Bayes@Lund 2019 Tutorials

Short tutorial

Adoption of Uncertainty Analysis in modern business environment is often challenging due to gaps in relevant skills and tooling (especially among decision makers). At the same time Excel is ubiquitous and can be used to build decision maker's intuition about uncertainties. This talk will introduce typical business problem faced by businesses and organizations on daily basis and showcase a...

29. Bayesian Deep Learning Applications in Biomedicine

Nikolay Oskolkov (Lund University, Department of Biology)

07/05/2019, 14:40

Bayes@Lund 2019 Meeting

Contributed talk

Next Generation Sequencing technologies gave rise to manifolds of Biomedical Big Data which is particularly manifested in the area of single cell transcriptomics where millions of cells are sequenced. Deep Learning (DL) is an ideal framework for analyzing large amounts of data and building predictive models for Clinical Diagnostics within the concept of Precision Medicine. Bayesian DL adds an...

16. How to deal with a noisy zero – a simple Bayesian treatment for small angle neutron scattering

Alexander Holmes (European Spallation Source ERIC)

07/05/2019, 15:30

Bayes@Lund 2019 Meeting

Contributed talk

Neutron scattering measurements are an ideal case for Bayesian analysis – statistics are limited, measurement time is expensive and there is often relevant background information.

I will present an example of small angle neutron scattering from superconducting vortex lattices. Most of the signal detected is irrelevant, and contributes nothing but noise to the final result. A Bayesian...

17. Bayesian inference of conformational ensembles from small-angle scattering data

Wojciech Potrzebowski (European Spallation Source, ERIC)

07/05/2019, 15:50

Bayes@Lund 2019 Meeting

Contributed talk

Small-angle scattering (SAS) uses x-ray or neutron scattering at small angles to investigate the structure of materials at the scale about 1-100nm. SAS is uniquely suited to study the conformational ensembles adopted by multidomain proteins. However, analysis is complicated by the limited information content in SAS data and care must be taken to avoid constructing overly complex ensemble...

18. Bayesian determination of the effect of a deep eutectic solvent on the structure of lipid monolayers

Andrew McCluskey (Diamond Light Source & University of Bath)

07/05/2019, 16:10

Bayes@Lund 2019 Meeting

Contributed talk

We present a unique insight from Bayesian-driven modelling for a series of lipid monolayers at the air-deep eutectic solvent (DES) interface using reflectometry measurements.
A chemically-consistent modelling approach shows that the lipid monolayers at the air-DES interface are similar to those on water, while removing the need for water-specific constraints.
Furthermore, the use of...

11. Automatic Learning of Summary Statistics for Approximate Bayesian Computation Using Deep Learning

Samuel Wiqvist (Lund University)

07/05/2019, 16:30

Bayes@Lund 2019 Meeting

Contributed talk

Learning summary statistics is a fundamental problem in Approximate Bayesian Computation (ABC). The problem of learning summary statistics is in fact the main challenge when applying ABC in practice, and affects the resulting inference considerably. Deep learning methods have previously been used to learn summary statistics for ABC. Here we introduce a novel deep learning architecture...