Bayesian Over-the-Air Computation

Bayesian perspective over time

Thomas Bayes, the founder of Bayesian vision, entered the University of Edinburgh in 1719 to study logic and theology. Returning in 1722, he worked with his father in a small church. He also was a mathematician and in 1740 he made a novel discovery which he never published, but his friend Richard Price found it in his notes after his death in 1761, reedited it and published it. But until L...

Over-the-air Function Computation in Sensor Networks

Many sensor applications are interested in computing a function over measurements (e.g., sum, average, max) as opposed to collecting all sensor data. Today, such data aggregation is done in a cluster-head. Sensor nodes transmit their values sequentially to a cluster-head node, which calculates the aggregation function and forwards it to the base station. In contrast, this paper explores the pos...

Approximate Bayesian Computation

Just when you thought it was safe to go back into the water, I’m going to complicate things even further. The Nielsen-Wakely-Hey [5, 3, 4] approach is very flexible and very powerful, but even it doesn’t cover all possible scenarios. It allows for non-equilibrium scenarios in which the populations from which we sampled diverged from one another at different times, but suppose that we think our ...

Approximation Bayesian Computation.

Approximation Bayesian computation [ABC] is an analysis approach that has arisen in response to the recent trend to collect data that is of a magnitude far higher than has been historically the case. This has led to many existing methods become intractable because of difficulties in calculating the likelihood function. ABC circumvents this issue by replacing calculation of the likelihood with a...

Advanced Bayesian Computation