Bayesian material flow analysis for systems with multiple levels of disaggregation and high dimensional data.

Material flow analysis (MFA) is used to quantify and understand the life cycles of materials from production to end of use, which enables environmental, social, and economic impacts and interventions. MFA is challenging as available data are often limited and uncertain, leading to an under-determined system with an infinite number of possible stocks and flows values. Bayesian statistics is an effective way to address these challenges by principally incorporating domain knowledge, quantifying uncertainty in the data, and providing probabilities associated with model solutions.

London taxi drivers exploit neighbourhood boundaries for hierarchical route planning.

Humans show an impressive ability to plan over complex situations and environments. A classic approach to explaining such planning has been tree-search algorithms which search through alternative state sequences for the most efficient path through states. However, this approach fails when the number of states is large due to the time to compute all possible sequences.

α-Synuclein Conformations in Plasma Distinguish Parkinson's Disease from Dementia with Lewy Bodies.

Aggregation of misfolded α-synuclein (aSyn) within the brain is the pathologic hallmark of Lewy body diseases (LBD), including Parkinson's disease (PD) and dementia with Lewy bodies (DLB). Evidence exists for aSyn "strains" - conformations with distinct biological properties. However, biomarkers for PD vs.