Use Bayes to interpret a broken radar alarm

Q: Use Bayes to interpret a broken radar alarm

This question evaluates Bayesian probabilistic reasoning, understanding of base rates, sensitivity/specificity, decision-theoretic trade-offs between false positives and misses, and practical competencies in model validation, metric selection, and handling data quality issues.

Q: How do I approach Analytics & Experimentation interview questions?

Analytics & Experimentation questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master analytics & experimentation interviews.

Question

Loading...

A “radar” system (or anomaly alarm) is suspected to be unreliable. You are asked to interpret its alerts and recommend how to operate it.

Given

Define the event of interest as E (e.g., “real intrusion/real aircraft present”) and an alarm A. You are given (or can estimate from logs):

Base rate: $P(E)$ (the event is rare)
Sensitivity/TPR: $P(A\mid E)$
False positive rate/FPR: $P(A\mid \neg E)$
(Optional) costs: cost of missing an event vs cost of investigating a false alarm

Questions

When the system triggers an alarm, what is the probability the event is real? (Compute $P(E\mid A)$ .)
Explain why a high TPR can still result in many false alarms in rare-event settings.
If you can tune an alarm threshold, how would you choose it? Discuss precision–recall tradeoffs and incorporate business costs.
Propose a statistically sound way to validate whether the radar is “broken” compared with a baseline (e.g., last month’s model or another sensor):
- What hypotheses would you test?
- What metrics would you monitor (primary + guardrails)?
- What data issues could bias your conclusion (label noise, delayed labels, non-stationarity)?

Use Bayes to interpret a broken radar alarm

Given

Questions

Solution

Comments (0)

Use Bayes to interpret a broken radar alarm

Overview

Given

Questions

Solution

Comments (0)