A twelve-week track on the probabilistic and statistical backbone every sensing result rests on — estimation, Bayesian inference, experimental design, and honest reporting. The throughline: an estimate without a defensible uncertainty is not a result. This is the methods course the other four lean on.
The 12-week arc
1 of 12 lectures published — each a self-contained read with a slide deck and runnable workbook. Start at the first published week and work forward.
| Wk | Theme & focus | Read |
|---|---|---|
| 01 | Estimation theory & the CRLB start here Bias, variance, and the best any estimator can do | read → slides · workbook |
| 02 | Bayesian inference for sensing Priors that earn their keep; posteriors over hidden states | planned |
| 03 | Gaussian processes for spatial fields Modelling a radio/sensor field with calibrated uncertainty | planned |
| 04 | Sensor fusion as inference Combining heterogeneous sources as one probabilistic model | planned |
| 05 | Hypothesis testing & reproducibility p-values, power, and the replication crisis in practice. Use the lab's own canon cases: TOST semantics (pass = equivalence within Δ; fail ≠ difference — the drift-trigger powered run), the flat-day KS ladder where 4× power SHRANK the effect 0.24→0.135 while p crossed 0.05 (significance-by-sample-size), and the Zaman AUC = 1.000 session-leakage audit | planned |
| 06 | Experimental design Factors, blocking, randomisation for field experiments | planned |
| 07 | Bootstrap & resampling Confidence intervals when the textbook assumptions fail | planned |
| 08 | Time-series & non-stationarity Drift and autocorrelation (the statistical side of WS501 wk 9) | planned |
| 09 | Calibration & uncertainty quantification Are your error bars honest? Reliability diagrams and coverage | planned |
| 10 | Causal thinking in field experiments Confounders, and what you can and cannot claim | planned |
| 11 | Honest statistical reporting CDFs, CIs, and effect sizes over point estimates | planned |
| 12 | Capstone A pre-registered analysis plan for a sensing experiment | planned |
Capstone
Week 12: take a real or planned sensing experiment and write a pre-registration — hypotheses, design, the exact analysis, and the uncertainty reporting — that a reviewer could hold you to.