Bayesian Statistics Calculator

Advanced Bayesian inference calculator with posterior probability computation and adaptive threshold calibration for market analysis.

Posterior Probability

Sophisticated Bayesian inference with configurable priors

Adaptive Thresholds

Dynamic calibration for optimal detection accuracy

Log-Space Computation

Numerical stability for complex calculations

Bayesian Parameters

Prior Probability (P(H))

Base Rate

0.1% 0.4% 10%

Market Context: The prior represents the base rate of pump events in the market. A lower prior (0.1-0.5%) is more conservative, while higher values (1-5%) are more aggressive.

Likelihood Parameters

True Positive Rate (Sensitivity)

80% 95% 99%

True Negative Rate (Specificity)

80% 98% 99%

Evidence Input

Detection Signal Strength

Number of Supporting Features

Computation Options

Use log-space computation for stability Enable adaptive threshold calibration Calculate confidence intervals

Bayesian Results

Configure parameters and click "Calculate Bayesian Inference" to see results

Bayesian Formula

Posterior Probability

P(H|E) = P(E|H) × P(H) / P(E)

P(H|E): Posterior probability (probability of hypothesis given evidence)

P(E|H): Likelihood (probability of evidence given hypothesis)

P(H): Prior probability (base rate of hypothesis)

P(E): Marginal likelihood (probability of evidence)

Market Application

Pump Detection Context

• H: Pump event is occurring
• E: Observed market signals
• P(H): Base rate of pump events (0.1-0.5%)
• P(E|H): Probability of signals given pump

Decision Threshold

When P(H|E) > threshold, trigger pump detection alert. The threshold is typically set based on risk tolerance and market conditions.

Advanced Bayesian Analytics

Our Bayesian implementation provides enterprise-grade statistical inference with adaptive thresholds and numerical stability for critical market surveillance applications.

Posterior Probability Adaptive Thresholds Log-Space Computation Confidence Intervals