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New Release: Lambda Bayesian Causality Detection & Event Forecasting

What’s New? 1. Causality Chain Detection Now automatically analyzes the causal probability that a positive jump (ΔΛC+) is followed by a negative jump (ΔΛC−). Also supports time-lagged causality analysis, visualizing how causal links evolve over different time windows. 2. Event Forecasting Functionality Added predict_next_event(): The model can now forecast whether the next structural transition will be a positive jump, negative jump, or stable (no significant change). Forecast logic is based on the recent sequence of ΔΛC events, with easy hooks for further extension. 3. Interactive Visualization Bar plots now show time-dependent causality (lag vs. probability). Jump events and predictions are clearly displayed for easier interpretation. 4. Modular, Extensible Design Event memory and prediction logic are structured for rapid customization—ready for ML, Markov, or Bayesian upgrades. Example Usage # After fitting the model and extracting event history: causality_prob = lambda3_ext.detect_causality_chain() next_event = lambda3_ext.predict_next_event() print(f"Causality Probability (Pos → Neg): {causality_prob:.2f}") print(f"Next Event Prediction: {next_event}") Causality probability by lag (bar chart) Causality Probability (Positive Jump → Negative Jump): 0.40 Predicted Next Event: stable Time-Dependent Causality (lag steps → P): {1: 0.4, 2: 0.0, 3: 0.2, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.2, 9: 0.0, 10: 0.0} Posterior distributions for jump/volatility parameters Model fit & event overlay plots Performance Colab A100 (CPU backend): 300 time steps × 4 params, 6000 samples, ~14 seconds No divergences, rapid convergence Supports large time-series (T > 1000) with additional tuning Want blazing speed? Use NumPyro backend on GPU Note: The demo runs great on both laptops and cloud GPUs. Colab A100 will give you extra headroom for bigger or more complex models. Why this matters Lambda³ is evolving beyond classic anomaly detection— Now you can detect AND anticipate structural regime shifts in complex systems. The code is 100% MIT Licensed. Try it out and let us know how you use or extend it! GITHUB Tags: #python #simulation #physics #computationalphysics #Bayesian

Jun 20, 2025 - 03:50

New Release: Lambda Bayesian Causality Detection & Event Forecasting

What’s New?

1. Causality Chain Detection

Now automatically analyzes the causal probability that a positive jump (ΔΛC+) is followed by a negative jump (ΔΛC−).

Also supports time-lagged causality analysis, visualizing how causal links evolve over different time windows.

2. Event Forecasting Functionality

Added predict_next_event():
The model can now forecast whether the next structural transition will be a positive jump, negative jump, or stable (no significant change).

Forecast logic is based on the recent sequence of ΔΛC events, with easy hooks for further extension.

3. Interactive Visualization

Bar plots now show time-dependent causality (lag vs. probability).

Jump events and predictions are clearly displayed for easier interpretation.

4. Modular, Extensible Design

Event memory and prediction logic are structured for rapid customization—ready for ML, Markov, or Bayesian upgrades.

Example Usage

# After fitting the model and extracting event history:
causality_prob = lambda3_ext.detect_causality_chain()
next_event = lambda3_ext.predict_next_event()

print(f"Causality Probability (Pos → Neg): {causality_prob:.2f}")
print(f"Next Event Prediction: {next_event}")

Causality probability by lag (bar chart)

Causality Probability (Positive Jump → Negative Jump): 0.40
Predicted Next Event: stable
Time-Dependent Causality (lag steps → P):
{1: 0.4, 2: 0.0, 3: 0.2, 4: 0.0, 5: 0.0, 6: 0.0, 7: 0.0, 8: 0.2, 9: 0.0, 10: 0.0}

Posterior distributions for jump/volatility parameters

Model fit & event overlay plots

Performance

Colab A100 (CPU backend): 300 time steps × 4 params, 6000 samples, ~14 seconds
No divergences, rapid convergence
Supports large time-series (T > 1000) with additional tuning
Want blazing speed? Use NumPyro backend on GPU

Note: The demo runs great on both laptops and cloud GPUs. Colab A100 will give you extra headroom for bigger or more complex models.

Why this matters

Lambda³ is evolving beyond classic anomaly detection—
Now you can detect AND anticipate structural regime shifts in complex systems.
The code is 100% MIT Licensed.
Try it out and let us know how you use or extend it!

GITHUB

Tags: #python #simulation #physics #computationalphysics #Bayesian