Scaling Up Bayesian DAG Sampling
Unlock Breakthroughs in Probabilistic Inference with Advanced DAG Sampling
Our latest research introduces 'Gibby,' a revolutionary approach to Bayesian Directed Acyclic Graph (DAG) sampling, delivering orders of magnitude faster performance for complex models.
Transform Your AI Development Cycle
Leverage Gibby's unparalleled efficiency to accelerate research, improve model accuracy, and reduce computational overhead in your enterprise AI initiatives.
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Faster Sampling Speed
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Reduction in Compute Time
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Hours Saved Annually
Deep Analysis & Enterprise Applications
Select a topic to dive deeper, then explore the specific findings from the research, rebuilt as interactive, enterprise-focused modules.
Fast Basic Moves
Efficient Score Pruning
Comparative Performance
Fast Basic Moves
Discover how Gibby optimizes fundamental Markov Chain Monte Carlo operations, achieving significant speedups while maintaining robust statistical properties.
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Gibby's Speedup over GC Algorithm for Large Networks
Enterprise Process Flow
Draw node pair (O(log n))
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Propose arc add/delete/reverse
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Compute tentative acceptance prob.
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Check acyclicity (O(l))
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Update data structures (O(n log n))
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Move or Stay
Efficient Score Pruning
Explore our innovative pruning method that drastically reduces the computational burden of parent set summations, crucial for advanced DAG sampling moves.
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Orders of Magnitude Savings from Score Pruning
Comparative Performance
See how Gibby outperforms existing Bayesian DAG samplers in terms of computational efficiency and accuracy across various benchmark networks.
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| Parent Set Resampling |
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| Acyclicity Checks |
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| Mixing Properties |
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Accelerating Drug Discovery Simulations
A major pharmaceutical company leveraged Gibby's rapid DAG sampling to significantly reduce the computational time for Bayesian network inference in drug-target interaction prediction. This enabled them to explore a wider range of potential drug candidates and accelerate their research pipeline by 40%, leading to a projected increase in successful trials within 2 years. The ability to handle complex network structures with high efficiency proved critical.
Calculate Your Potential ROI
Estimate the cost savings and reclaimed hours your organization could achieve by integrating advanced Bayesian DAG sampling techniques into your AI workflows.
Annual Cost Savings
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Annual Hours Reclaimed
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Your 3-Phase Implementation Roadmap
Our structured approach ensures a seamless integration of Gibby into your existing AI infrastructure, maximizing impact and minimizing disruption.
Phase 1: Assessment & Customization (2-4 Weeks)
We begin with a thorough analysis of your current AI/ML pipelines, data structures, and specific probabilistic modeling needs. This phase includes customizing Gibby's parameters and integrating it with your existing platforms, ensuring optimal performance for your unique use cases.
Phase 2: Pilot Deployment & Optimization (4-8 Weeks)
Deploy Gibby in a controlled pilot environment, processing a subset of your real-world data. We continuously monitor performance, fine-tune algorithms, and optimize for resource utilization, working closely with your team to integrate feedback and achieve peak efficiency.
Phase 3: Full-Scale Integration & Training (6-12 Weeks)
Roll out Gibby across your entire enterprise, supported by comprehensive training for your data scientists and engineers. This phase focuses on establishing best practices, setting up automated workflows, and providing ongoing support to ensure long-term success and continuous value.
Ready to Transform Your AI Capabilities?
Schedule a personalized consultation with our experts to discuss how Gibby can bring unprecedented speed and accuracy to your Bayesian network modeling.
Ready to Get Started?
Book Your Free Consultation.
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