Deducing Queueing from Transactional Data: The Queue Inference Engine, Revisited

Deducing Queueing from Transactional Data: The Queue Inference Engine, Revisited

Larson [1] proposed a method to statistically infer the expected transient queue length during a busy period in 0(n 5) solely from the n starting and stopping times of each customer's service during the busy period and assuming the arrival distribution is Poisson. We develop a new O(n 3) algorithm which uses this data to deduce transient queue lengths as well as the waiting times of each custom...

The Queue Inference Engine: Deducing Queue Statistics from Transactional Data

Deducing Underlying Mechanisms from Protein Recruitment Data

By using fluorescent labelling techniques, the distribution and dynamics of proteins can be measured within living cells, allowing to study in vivo the response of cells to a triggering event, such as DNA damage. In order to evaluate the reaction rate constants and to identify the proteins and reactions that are essential for the investigated process, mechanistic models are used, which often co...

The Bayes Inference Engine

We are developing a computer application, called the Bayes Inference Engine, to provide the means to make inferences about models of physical reality within a Bayesian framework. The construction of complex nonlinear models is achieved by a fully object-oriented design. The models are represented by a data-flow diagram that may be manipulated by the analyst through a graphicalprogramming enviro...

Towards The Automated Inference Of Queueing Network Models From High-Precision Location Tracking Data

Traditional methods for deriving performance models of customer flow in real-life systems are manual, timeconsuming and prone to human error. This paper proposes an automated four-stage data processing pipeline which takes as input raw high-precision location tracking data and which outputs a queueing network model of customer flow. The pipeline estimates both the structure of the network and t...