Bayesian Inference

Confidence is cheap. Calibration is expensive. That is the uncomfortable lesson behind a new arXiv paper on earthquake source inversion, a domain that sounds safely remote until one notices the pattern: a complex physical simulator, uncertain model inputs, high-dimensional observations, and a decision-maker who wants a probability distribution rather than a shrug.1 Replace “earthquake waveform” with “financial stress scenario,” “robot sensor stream,” “industrial digital twin,” or “clinical simulator,” and the problem becomes less geological and more familiar. ...

Drilling is where optimism receives an invoice. In mineral exploration, maps can look promising, models can look elegant, and geophysical anomalies can glow like destiny on a consultant’s slide deck. Then the drill rig arrives. A few expensive holes later, the anomaly turns out not to be an economic mineral system, the team moves to the next target, and everyone quietly files the failed interpretation under “learning.” Very scientific. Very costly. ...

A hospital does not need a chatbot that sounds certain. It needs a system that knows when certainty would be irresponsible. That sounds obvious until one remembers how most AI demos behave: fluent answer first, caveat somewhere after the damage has already put on shoes. In clinical decision support, this is not a stylistic defect. It is an operating risk. A model can be wrong in many ways, but the most dangerous version is the confidently wrong one: the triage answer that should have been escalated, the medication suggestion that should have been checked, the risk score that looks clean only because the system has no vocabulary for doubt. ...

TL;DR for operators Labels are the awkward invoice behind modern alignment. RLHF looks elegant in diagrams: generate outputs, ask humans which one is better, train a reward model, optimise the policy, repeat until everyone pretends the reward model is civilisation. In practice, most preference comparisons are not equally useful. Some are obvious. Some are redundant. Some teach the model almost nothing except that annotator budgets have a sense of humour. ...

TL;DR for operators Jaehyung Choi’s paper does not offer a new trading strategy, volatility forecast, or backtest that makes the Sharpe ratio stand up and sing.1 Its contribution is more structural: it builds an information-geometric framework for Lévy processes, the family of stochastic processes often used when financial returns refuse to behave like polite Gaussian increments. ...