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.
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...
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...
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...
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...
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,...
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...
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...
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...
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...
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...
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...
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...
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...
