GP-SUM. Gaussian Processes Filtering of non-Gaussian Beliefs

This work studies the problem of stochastic dynamic filtering and state propagation with complex beliefs. The main contribution is GP-SUM, a filtering algorithm tailored to dynamic systems and observation models expressed as Gaussian Processes (GP), and to states represented as a weighted sum of Gaussians. The key attribute of GP-SUM is that it does not rely on linearizations of the dynamic or observation models, or on unimodal Gaussian approximations of the belief, hence enables tracking complex state distributions. The algorithm can be seen as a combination of a sampling-based filter with a probabilistic Bayes filter. On the one hand, GP-SUM operates by sampling the state distribution and propagating each sample through the dynamic system and observation models. On the other hand, it achieves effective sampling and accurate probabilistic propagation by relying on the GP form of the system, and the sum-of-Gaussian form of the belief. We show that GP-SUM outperforms several GP-Bayes and Particle Filters on a standard benchmark. We also demonstrate its use in a pushing task, predicting with experimental accuracy the naturally occurring non-Gaussian distributions.Comment: WAFR 2018, 16 pages, 7 figure

The impact of Participatory Budgeting on health and wellbeing:A scoping review of evaluations

Background: Participatory budgeting (PB), citizens deliberating among themselves and with officials to decide how to allocate funds for public goods, has been increasingly implemented across Europe and worldwide. While PB is recommended as good practice by the World Bank and the United Nations, with potential to improve health and wellbeing, it is unclear what evaluations have been conducted on the impact of PB on health and wellbeing. Methods: For this scoping review, we searched 21 databases with no restrictions on publication date or language. The search term ‘participatory budget’ was used as the relevant global label for the intervention of interest. Studies were included if they reported original analysis of health, social, political, or economic and budgetary outcomes of PB. We examined the study design, analysis, outcomes and location of included articles. Findings are reported narratively. Results: From 1458 identified references, 37 studies were included. The majority of evaluations (n = 24) were of PB in South America, seven were in Europe. Most evaluations were case studies (n = 23) conducting ethnography and surveys, focussing on political outcomes such as participation in PB or impacts on political activities. All of the quantitative observational studies analysing population level data, except one in Russia, were conducted in South America. Conclusion: Despite increasing interest in PB, evaluations applying robust methods to analyse health and wellbeing outcomes are scarce, particularly beyond Brazil. Therefore, implementation of PB schemes should be accompanied by rigorous qualitative and quantitative evaluation to identify impacts and the processes by which they are realised