Benchmark Results

All measurements below were captured using the methodology described in Benchmark Methodology. Unless stated otherwise:

• Expected run length λ = 150

• 2 warm-up runs, 10 timed runs

• Dataset sizes: 1,000 / 10,000 / 100,000 observations

Summary (100k Observations, Online Mode)

Model	Throughput (obs/s)	Description
Bernoulli-Beta	33	573	Binary outcomes (fastest model)
Gaussian-NIG	25	063	Continuous data (mean & variance unknown)
Gamma-Gamma	24	290	Positive continuous amounts/durations
Student-t (Fixed ν)	21	796	Robust continuous w/ known ν
Poisson-Gamma	21	402	Event counts per interval
Binomial-Beta	14	599	k successes out of N trials
Student-t (Grid ν)	3	471	Robust continuous w/ ν inference

Offline mode delivers ~1.3–1.5× higher throughput for every model due to reduced Python call overhead and better cache locality.

Gaussian-NIG

Use case: general-purpose continuous data with unknown mean and variance.

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0322	31	009	0.1%
1k	Offline	0.0228	43	835	1.7%
10k	Online	0.3838	26	057	0.1%
10k	Offline	0.2882	34	699	0.1%
100k	Online	3.9899	25	063	0.9%
100k	Offline	3.0314	32	988	0.5%

Student-t (Fixed ν)

Use case: robust changepoint detection when degrees of freedom are known.

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0377	26	504	0.5%
1k	Offline	0.0280	35	714	0.2%
10k	Online	0.4507	22	189	0.3%
10k	Offline	0.3520	28	409	0.5%
100k	Online	4.5880	21	796	0.2%
100k	Offline	3.6040	27	747	0.1%

Student-t (Grid ν)

Use case: robust detection when ν is unknown and inferred from a grid.

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.2261	4	422	0.2%
1k	Offline	0.2150	4	652	0.4%
10k	Online	2.8238	3	541	0.1%
10k	Offline	2.7136	3	685	0.1%
100k	Online	28.8063	3	471	0.5%
100k	Offline	27.7352	3	606	0.1%

Bernoulli-Beta

Use case: binary observations (success/failure, on/off).

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0281	35	608	0.5%
1k	Offline	0.0222	45	013	0.2%
10k	Online	0.2952	33	871	1.8%
10k	Offline	0.2468	40	521	0.1%
100k	Online	2.9786	33	573	1.0%
100k	Offline	2.5797	38	765	1.0%

Binomial-Beta

Use case: proportion data (k successes out of N trials).

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0574	17	422	0.3%
1k	Offline	0.0429	23	314	0.2%
10k	Online	0.6762	14	788	0.2%
10k	Offline	0.5307	18	843	0.2%
100k	Online	6.8500	14	599	0.1%
100k	Offline	5.3848	18	571	0.1%

Poisson-Gamma

Use case: event counts per time interval.

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0399	25	039	0.5%
1k	Offline	0.0261	38	249	8.0%
10k	Online	0.4592	21	777	0.4%
10k	Offline	0.3212	31	131	0.1%
100k	Online	4.6725	21	402	0.1%
100k	Offline	3.2575	30	698	0.1%

Gamma-Gamma (Fixed Shape)

Use case: positive continuous data (durations, lifetimes, transaction amounts).

Size	Mode	Median (s)	Throughput (obs/s)	CV%
1k	Online	0.0340	29	448	0.2%
1k	Offline	0.0243	41	103	0.2%
10k	Online	0.3908	25	586	0.1%
10k	Offline	0.2950	33	894	1.0%
100k	Online	4.1170	24	290	0.1%
100k	Offline	3.1020	32	237	0.1%

Key Observations

• Most models sustain 20k–35k obs/s even at 100k points.

• Offline mode yields 30–50% better throughput thanks to reduced Python overhead.

• CV% is usually < 1%, indicating stable, repeatable performance across runs.

• Student-t Grid trades speed for robustness; expect a 6–7× slowdown versus fixed-ν.