bayes4psy

Research in psychology generates complex data and often requires unique statistical analyses. These tasks are often very specific, so appropriate statistical models and methods cannot be found in accessible Bayesian tools. As a result, the use of Bayesian methods is limited to researchers and students that have the technical and statistical fundamentals that are required for probabilistic programming. Such knowledge is not part of the typical psychology curriculum and is a difficult obstacle for psychology students and researchers to overcome. The goal of the bayes4psy package is to bridge this gap and offer a collection of models and methods to be used for analysing data that arises from psychological experiments and as a teaching tool for Bayesian statistics in psychology. The package contains the Bayesian t-test and bootstrapping along with models for analysing reaction times, success rates, and tasks utilizing colors as a response. It also provides the diagnostic, analytic and visualization tools for the modern Bayesian data analysis workflow

An efficient explanation of regression and classification models’ predictions

Providing an explanation for prediction models is an important part of knowledge discovery. It helps with a quicker and better understanding of the model and increases the user's level of trust in the model's predictions. We focus on methods, which assign to each input feature its contribution to the model's prediction. Most such methods are model-specific. However, some can be used for any type of model, thus simplifying their use and enabling the comparison of different types of models. Existing general methods do not take into account the interactions across all subsets of input features and in certain cases fail to provide a proper explanation. We propose a general method, which takes all interactions into account and deals with the shortcomings of existing methods. We prove that the proposed method is related to the Shapley value - a well-known concept from game theory. We deal with the resulting exponential time complexity by using an approximation. We use selective sampling and quasi-random sampling to further improve the efficiency of the approximation algorithm. We also propose a mechanism, which allows the user to select a tradeoff between the total running time and expected error. Synthetic and real-world data sets are used and several different classification and regression models are applied to empirically show the practical utility of the proposed method. We describe how the method was applied to a real-world breast cancer recurrence problem and how oncologists confirmed the method's usefulness. We also list other successful application of the proposed method. We also conducted an experiment, during which we tested the users' ability to learn from examples (with or without an explanation) and make predictions for new and unknown instances. The results reveal that the explanation with contributions of input features help and increase the accuracy of the user's predictions

A Bayesian approach to time-varying latent strengths in pairwise comparisons.

The famous Bradley-Terry model for pairwise comparisons is widely used for ranking objects and is often applied to sports data. In this paper we extend the Bradley-Terry model by allowing time-varying latent strengths of compared objects. The time component is modelled with barycentric rational interpolation and Gaussian processes. We also allow for the inclusion of additional information in the form of outcome probabilities. Our models are evaluated and compared on toy data set and real sports data from ATP tennis matches and NBA games. We demonstrated that using Gaussian processes is advantageous compared to barycentric rational interpolation as they are more flexible to model discontinuities and are less sensitive to initial parameters settings. However, all investigated models proved to be robust to over-fitting and perform well with situations of volatile and of constant latent strengths. When using barycentric rational interpolation it has turned out that applying Bayesian approach gives better results than by using MLE. Performance of the models is further improved by incorporating the outcome probabilities

Bayesian Lasso and multinomial logistic regression on GPU

<div><p>We describe an efficient Bayesian parallel GPU implementation of two classic statistical models—the Lasso and multinomial logistic regression. We focus on parallelizing the key components: matrix multiplication, matrix inversion, and sampling from the full conditionals. Our GPU implementations of Bayesian Lasso and multinomial logistic regression achieve 100-fold speedups on mid-level and high-end GPUs. Substantial speedups of 25 fold can also be achieved on older and lower end GPUs. Samplers are implemented in OpenCL and can be used on any type of GPU and other types of computational units, thereby being convenient and advantageous in practice compared to related work.</p></div