Bayesian inference in networks
Bibliographic record
Abstract
Bayesian inference is a method that can be used to estimate an unknown and/or unobservable parameter based on evidence that is accumulated over time.In this thesis, we apply Bayesian inference techniques in the context of two network-based problems.First, we consider multi-target tracking in networks with superpositional sensors, i.e., sensors that generate measurements equal to the sum of individual contributions of each target.We derive a tractable form for a novel moment-based multi-target filter called the Additive Likelihood Moment (ALM) filter.We show, through simulations, that our particle approximation of the ALM filter is more accurate and computationally efficient than Markov chain Monte Carlo-based particle methods to perform radio-frequency (RF) tomographic tracking of multiple targets.The second problem we study is multi-path available bandwidth estimation in computer networks.We propose a probabilistic-rate-based definition for the available bandwidth, probabilistic available bandwidth (PAB), that addresses flaws of the classical utilizationbased definition and existing estimation tools.We design a network-wide estimation tool that uses factor graphs, belief propagation and adaptive sampling to minimize the overhead.We deploy our tool on the Planet Lab network and show that it can produce accurate estimates of the PAB and achieve significant gains (over 70%) in terms of measurement overhead and latency over a popular estimation tool (Pathload).We extend our tool to i) track PAB in time and ii) use chirps to further reduce the number of required measurements by over 80%.Our simulations and online experiments demonstrate that our tracking algorithm is more accurate than block-based approaches without any significant additional complexity.Above all, I want to thank my supervisor Mark Coates, without whom realizing this thesis would have never been possible.For the past seven years, he has taught me everything I know about academic research in a respectful, collaborative and friendly, yet professional, work environment.I could not have asked for better guidance or for a more comprehensive advisor.I am very grateful for all the technical help I have received from people in my lab over the years.In particular, Prof. Michael Rabbat who has helped me as his own student and treated me as a friend from the very first day he arrived at McGill.His availability, precious advice and contributions have played a significant role in most parts of this thesis.Santosh Nannuru for sharing his Markov Chain Monte Carlo-based algorithm and simulation results and helping out on paper submission.Xi Chen for his time and explanations about radiofrequency tomographic target tracking.My research would not have been possible without the financial support I have received from FQRNT (Fonds Québécois de la Recherche sur la Nature et les Technologies), the MITACS (Mathematics in Information Technology and Complex Systems) internship program and Mark Coates.For making my life so much easier on a regular basis and allowing me to focus on research, I would like to thank Carrie Serban at the SYTAcom office.For their moral support and encouragements not to give up when it all seemed impossible, I would be forever grateful to all my friends.A special mention to Daniel, Mike and Louis-Philippe who helped me proof-reading this thesis to have their name appear in this section.Un merci particulier à la personne la plus importante dans ma vie, Marie-Ève, pour son intérêt des plus sincères et sa motivation quotidienne.Finalement, je tiens à exprimer ma gratitude envers mes parents qui ont toujours respecté et supporté mes choix, incluant celui un peu fou de compléter ce doctorat.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.012 | 0.045 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.007 | 0.006 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".