Bayes Days 2019 will occur on October 21st -22nd at the Liverpool Institute for Risk and Uncertainty. A convocation of mutual support for Bayesians and would-be Bayesians, Bayes Days is built for who have been stymied by intractable problems, wonky software, problematic data, or just their own inexperience. Come and tell us your problems so together we can find solutions and collaborations.
If you're interested please register using the link above. If you would like to make an oral or poster presentation please also send an abstract (300 words or less) and self-label with one of the items provided, along with your name, affiliation, email, and phone number.
If you would like to offer a training workshop, please send a description alongside your registration (less than a page) explaining the content, intended audience, required resources, planned duration (2 hours or 4 hours) and maximum number of participants.
Schedule
Monday 21st October
09:00 | Introduction to Bayes Days | |
09:30 | Ulrika Sahlin | Tutorial: To Bayes or Bootstrap - that is the question |
11:00 | Alex Sutton & Rhiannon Owen | Introduction Bayesian Meta-Analysis |
12:00 | Lunch Break | |
13:00 | Alex Sutton | Tutorial: Bayesian Meta-Analysis |
14:30 | Diego Estrada-Lugo | Modelling Logical Statements with Credal Networks |
15:30 | David Hughes | Modelling Multiple Longitudinal Markers in Large Datasets using Variational Bayes |
Tuesday 22nd October
10:00 | Yu Chen | TBA |
11:00 | Anna Auer-Fowler | A Bayesian mixture model for inferring B cell specificity for vaccines |
12:00 | Lunch Break | |
13:00 | Peter Hristov | Probabilistic Numerics |
14:00 | Leonel Hernandez | TBA |
15:00 | Brendan McCabe | Approximate Bayesian Forecasting |
Tutorials featured in Bayes Days 2019
How important is the organization of the data?
Leonel Hernandez
The use of data analysis methods starts with the focus on the data organization, which can be the decision point of whether or not the data collected is useful. This shows that the input data of any probabilistic and machine learning technique is key to find the expected results. In this research work, a machine learning technique is presented to address the issue of reliability and safety of electronic systems. This work focuses on accurate prognostic of remaining useful lifetime (RUL). In tests performed, the methodology utilized is the support vector regression (SVR) that learns from electronic signals. SVR was chosen among other methodologies, e.g. neural networks. The prognostics on the RUL has shown an accuracy of practically 100%, and this is why is worth to ask if Bayes could offer a better prediction or it all depends on each case.To Bayes or Bootstrap - that is the question
Ulrika Sahlin
This tutorial compares Bayesian inference with the Bootstrap. Bootstrap is a method that frequentists often use to get estimation errors or probability intervals to their estimates, when that doesn’t follow automatically from the analysis. Thus, when you want things you always can get by Bayesian inference. I will discuss in what way it may be easier or more difficult to embrace a bootstrap compared to a Bayesian analysis using general mixed models for illustration. My conclusion is that Bayesian inference is a coherent principles for inference and more straightforward to implement than bootstrap added to a frequentist analysis.Probabilistic Numerics
Peter Hristov
Numerical algorithms, such as methods for the numerical solution of integrals and ordinary differential equations, among others can be interpreted as estimation rules. They estimate the value of an unobservable quantity, given the result of tractable computations. Thus, these methods perform inference, and are consequently accessible to the formal frameworks of probability theory. A probabilistic numerical method is a numerical algorithm that takes in a probability distribution over its inputs, and returns a probability distribution over its output. This talk will focus on introducing the field of probabilistic numerics and some applications.Modelling logical statements with credal networks
Diego Estrada-Lugo
Fault Tree Analysis (FTA) is a deductive method to find an undesired event by means of Boolean logic. This methodology has been implemented for decades in reliability analyses of complex engineering systems like the nuclear and oil industries to mention a few. The popularity of this method resides in the logical approach in which the failure event is studied and the simplicity of the relatively small amount of data that is required to model the events and components of the system. In this way, a failure probability of the so-called Top Event can be predicted and studied with relative ease. Despite the FTA success, there are several limitations that restrict the scope of the analysis and modellers had to explore another option. One of them is the Bayesian network (BN) method (or its imprecise probability version Credal networks, CNs), which consist in a probabilistic graphical model to study and understand the dependencies of events with epistemic uncertainty associated. The method allows to perform not only prognostic (like FTA) but also diagnostic analysis of specific components in the system. Furthermore, the CNs can incorporate new information in the form of evidence to update the probabilistic distributions of the model. However, given the significant advantages of CN over FTA, the latter is still very widely implemented. In this presentation, the principles about both methods are revealed and compared by means of didactic examples. and analogies and, to facilitate the discussion, we will ask some questions like why FTA is still widely used even though there are options, like CNs, that can produce generalised analyses? How much is too much in terms of the complexity of a tool that makes it hard to implement in the industry?
Modelling Multiple Longitudanal markers in Large Datasets using Variational Bayes
David Hughes
Data on multiple variables are now collected routinely at repeated visits in many clinical research areas. Mixed models are a popular way of analysing trends in longitudinal data. There is now increasing access to very large datasets, with both large numbers of patients, and large numbers of predictors. In these settings the current modelling approaches for mixed models can be very computationally intensive.
Mean Field Variational Bayes (MFVB) is a technique commonly used in Computer Science, which aims to find approximations to the posterior likelihood for Bayesian Models. In many settings these solutions have been shown to be very accurate and can be calculated in a fraction of the time required for equivalent MCMC models.
In this talk we describe how MFVB approaches can be used to estimate Bayesian Multivariate Mixed Models. I’ll discuss some of the computational obstacles I’ve faced, my current attempts to overcome them, and hopefully give opportunity for the audience to suggest better ones! We compare the time taken to estimate the models, and the accuracy of model parameter estimates through a variety of simulation studies and also through application to a large diabetes dataset aiming to model variables linked to the risk of developing Sight Threatening Diabetic Retinopathy.
The MFVB approach for Bayesian Mixed Models allows fast and approximate inference to be made in a variety of clinical models.
Location
Bayes Days is held in the Risk Institute Seminar Room in the Chadwick Building at the end of Peach Street in the heart of the University of Liverpool campus. Use the south entrance to the Chadwick Building; other entrances have no access to the Risk Institute. When you enter the building, you'll see the Muspratt Lecture Theatre. Turn left and enter the brown door, and follow signs to the Risk Institute Seminar Room.
Chadwick BuildingUniversity of Liverpool
Latitude: 53.404110 / 53°24'14.8"N,
Longitude: -2.964600 / 2°57'52.6"W
What3words: curiosity.memory.daring
Postal Address:
Institute for Risk and Uncertainty, University of Liverpool
Peach Street, Chadwick Building
L7 7BD Click here for directions
Tutorials featured in Bayes Days 2018
Prior knowledge, proportions and probabilities of probabilities
Alejandro Diaz, Institute for Risk and Uncertainty, University of Liverpool, UK
Which proportion is higher: 5 out of a 10 or 400 out of 1000? The answer seems obvious. But if these proportions were number of successes divided by number of trials, would you still think the same? Is a baseball player who achieved 5 hits out of 10 chances throughout his career, better than one who achieved 400 hits out of 1000 chances? In this introductory tutorial, we will see how Bayesian inference helps us add context in order to make decisions. The key will reside on representing prior knowledge using a probability distribution for probabilities: the very famous and elegant beta distribution.Bayesian linear regression and hierarchical models
Alfredo Garbuno, Institute for Risk and Uncertainty, University of Liverpool, UK
Bayesian data analysis allows researchers to conduct probabilistic inference about non-observable quantities in a statistical model. This introductory workshop is aimed at those interested in applying the Bayesian paradigm in their data analysis tasks.The tutorial will start with Bayesian linear regression models, and will provide guidelines for probabilistic enhancement to the model's complexity. This improvement will lead to the hierarchical regression model in which the Bayesian paradigm allows for a more flexible model, whilst providing a natural mechanism to prevent over-fitting. The session will present a classical Bayesian regression problem which can be followed through Python notebooks.
Quantifying uncertainty using data and experts
Ullrika Sahlin,Centre for Environmental and Climate Research, Lund University, SE
This tutorial introduces some of the basic principles to quantify uncertainty by Bayesian probability. I will demonstrate a way to quantify uncertainty by integrating expert’s knowledge and data. Participants can follow practical examples in R, using existing R packages for expert’s elicitation (SHELF) and sampling from the posterior (rjags requiring JAGS). The first example is a simple risk classification problem under sparse information and several experts with differing judgements. The second example is the familiar task to quantify uncertainty in input parameters of an assessment model using different sources of information and where uncertainty in assessment output matters.
Approximate Bayesian computation (ABC)
Brendan McCabe, Economics, Finance and Accounting, University of Liverpool, UK
This tutorial looks at how to do Bayesian Inference when it is too difficult to calculate the true likelihood and hence the exact posterior. (This is a Bayesian version of frequentist ‘indirect inference’ really.) We use model based summary statistics to match simulations form the assumed (difficult) model with the actual data at hand. Conventional approaches to ABC emphasize the role of parameter estimation but, in time series problems, forecasting is often the focus of attention and so it is to this dimension we direct our efforts. The role of Bayesian consistency is highlighted.
Generating samples from strange probability distributions
Peter Green, Institute for Risk and Uncertainty, University of Liverpool, UK
When conducting a probabilistic analysis, we often end up having to generate samples from a probability distribution. This, for example, is a crucial part of Monte Carlo simulations. For better-known probability distributions (Gaussian, uniform etc.), some simple tricks allow us to generate samples without too much difficulty. For the more ‘strange-looking’ distributions – which commonly arise in a Bayesian analysis – the problem becomes more difficult. This tutorial describes methods which can be used to generate samples from generic probability distributions. They often form an essential part of a Bayesian analysis. The tutorial is aimed at beginners, and will cover basic sampling algorithms before describing Markov chain Monte Carlo (MCMC) and importance sampling algorithms. Sample Matlab code will also be provided.