Tag
sampling
- May 27, 2026 Head-Based Sampling: Where the Decision Belongs and What It Costs How probabilistic and rate-limiting samplers make the keep-or-drop call at trace start, why the decision must be consistent across the trace, and how the sampled flag makes it authoritative downstream. observability
- Apr 21, 2026 The Overhead Budget of a Sampling Profiler Where the cost of continuous profiling actually goes, why per-sample cost and sample rate multiply, and how aggregation location changes the bill. observability
- Apr 3, 2026 Metropolis-Hastings: Detailed Balance and Proposal Design How a Markov chain is built so that an unnormalized density becomes its stationary distribution, what the acceptance ratio compensates for, and when a correctly implemented sampler still mixes badly. machine learning
- Sep 18, 2025 Merging Profiles Across a Fleet Without Losing the Tail Why summing per-host profiles answers a different question than profiling the fleet, how weight normalization changes the answer, and what a merged profile cannot tell you. observability
- Sep 4, 2025 Sparse Sampling and the Limits of Recovery Why a sub-Nyquist sample count can still determine a sparse signal, what the restricted isometry property guarantees, and where the guarantee stops being useful. scientific computing
- Aug 18, 2025 Counter Resets and the Extrapolation Inside rate() Why a monotonic counter loses its meaning when a process restarts, how reset detection reconstructs the increment, and what the edge extrapolation in a rate query assumes. observability
- Jul 28, 2025 Sampled Allocation Profiling and the Cost of Interposing on malloc How heap profilers attribute live memory to allocation sites, why sampling by size needs corrections the CPU analogue does not, and what interposition itself costs. observability
- Dec 23, 2024 How Sampling Rules Decide Which Evidence Survives The telemetry left after an incident is a sample drawn by a rule chosen before the question was asked. Tail sampling, log thresholds, and retention turn the surviving data into a biased census. observability
- Oct 28, 2024 Scrape Interval, Lookback Delta, and the Resolution of a Rate How sampling period sets the highest frequency a rate graph can show, why aliasing turns a steady signal into a beat pattern, and what lookback staleness does at the edges. observability
- Sep 6, 2024 Classifier-Free Guidance as an Interpolation of Two Predictions How a single conditional model can be pushed toward its conditional distribution by extrapolating away from its unconditional prediction, and what that costs. deep learning
- Aug 23, 2024 False Negatives and Batch Composition Every off-diagonal entry in a contrastive batch is treated as a negative, including entries that are semantically identical; the resulting pressure shapes the space and is controlled by what goes into the batch. deep learning
- Apr 28, 2024 Quantiles from Finite Samples and Their Confidence Bands What a p99 computed from a few hundred observations actually estimates, how the order statistic maps to a binomial interval, and why tail claims need sample sizes that frequently do not exist. performance engineering
- Sep 7, 2023 Reverse-Time SDEs and the Probability-Flow ODE How the time-reversed diffusion process is defined by the score, why a deterministic ODE shares its marginals, and what the two formulations trade against each other. deep learning
- Aug 7, 2023 Sampling Error, Skid, and How Many Samples a Profile Needs Why a profile is a binomial estimate, how instruction skid biases samples toward long-running instructions, and what confidence a given sample count actually supports. observability
- Jul 11, 2023 Adaptive Sampling: Feedback Control over Span Volume How a sampler adjusts its rate from observed traffic instead of a fixed configuration, why per-operation rates are needed, and the statistical damage caused by a rate that keeps changing. observability
- May 13, 2023 Low-Volume Objectives and the Distortion of Counting Qualifiers Why a 99.9% target on a few hundred requests a day cannot be estimated from the data, and how qualifier counting and aggregation windows change the answer. observability
- May 10, 2023 Sample-Based Profiling and Stack Walking in Compiled Code How a runtime learns where time goes by interrupting threads rather than instrumenting them, what a walk over compiled frames must decode, and why sampled profiles are biased in ways counters are not. language runtimes
- Jan 12, 2023 Sampling Allocation Profilers and the Statistics of Estimates How a profiler that records one allocation in a few thousand still reports accurate totals, why the sampling must be size-weighted, and how the confidence interval is computed. memory systems
- Dec 29, 2022 Sampling Rate, Aliasing, and the Folding of the Spectrum Why the Nyquist criterion constrains the signal rather than the converter, how aliasing folds frequencies arithmetically, and what reconstruction actually requires. scientific computing
- Aug 30, 2022 Inverse-Transform Sampling and the Generalized Inverse of the CDF Why mapping a uniform variate through the quantile function produces the right distribution, what the discrete case changes, and where floating-point resolution destroys the tails. scientific computing
- Jun 15, 2022 Tail-Based Sampling: Buffering Traces Until the Outcome Is Known Why deciding after the trace completes requires holding spans in memory, how the buffering window and the trace-ID routing rule interact, and where the pipeline loses traces it intended to keep. observability
- Apr 16, 2022 Signal-Driven Sampling: How a Profiler Captures a Stack Without Instrumenting Code How a timer, a signal handler, and an async-signal-safe buffer produce periodic stack samples, and why the delivery path decides what a sample can mean. observability