Conditional particle filters (CPFs) with backward/ancestor sampling are powerful methods for sampling from the posterior distribution of the latent states of a dynamic model such as a hidden Markov model. However, the performance of these methods deteriorates with models involving weakly informative observations and/or slowly mixing dynamics. Both of these complications arise when sampling finely time-discretized continuous-time path integral models, but can occur with hidden Markov models too. Multinomial resampling, which is commonly employed with CPFs, resamples excessively for weakly informative observations and thereby introduces extra variance. Furthermore, slowly mixing dynamics render the backward/ancestor sampling steps ineffective, leading to degeneracy issues. We detail two conditional resampling strategies suitable for the weakly informative regime: the so-called “killing” resampling and the systematic resampling with mean partial order. To avoid the degeneracy issues,
Understanding the uncertainty of model parameters is crucial for building predictive models. Within the field of spontaneous ignition a slight variation in the model parameters can cause a significant variation in our ability to determine if ignition occurs. We consider this problem through an application to the steel industry. A byproduct of the steelmaking process is stockpiled where oxidation can induce ignition. The resulting ignition process sinters the filter improving the durability. Understanding this process requires careful modelling and consideration of the uncertainty in the reaction kinetics. We examine some experimental data on the filter cake to determine these reaction kinetics. Due to the complex nature of the filter cake, standard estimation techniques are difficult to apply and the uncertainty in our parameters cannot be an input into the larger stockpiles. We apply a Bayesian framework for parameter estimation that considers a distribution for the parameters rather
SMCP3 is a new family of algorithms for solving sequential Bayesian inference problems with probabilistic programming. The AI is unique in that it outputs explanations for data, and estimates how accurate those explanations are.
Internet of Things (IoT) networks consist of sensing devices and gateways. Specifically, these devices monitor an environment to obtain measurements of a physical quantity such as temperature or the location of a target. A gateway or server is then required to collect and compute sensory data from devices. A critical issue in an IoT network is that devices have limited operation time due to the lack of energy. This affects the amount of data collected by devices and data processed by a gateway. To this end, prior works have considered powering devices using energy sources such as solar or wirelessly via Radio Frequency (RF) signals. Another issue is ensuring sensed data is processed quickly to infer any events. To address this issue, many works have considered installing computational resources at the edge of an IoT network.
Given the above issues, this thesis first considers a Hybrid Access Point (HAP) that charges one or more energy harvesting devices via RF signals. These devices th