method
active
method:heuristic-search-for-optimal-time-series-markov-conditional-independenceHeuristic Search for Optimal Time Series (Markov + Conditional Independence)
Iterative procedure searching token counts in [50,100,...,1000] to find concatenation of (C)ARR satisfying IIT's Markov and conditional independence assumptions.
Neighborhood — ranked by edge-count
Concepts (2)
concept
- Markov PropertyaboutAssumption required by IIT 3.0/4.0 and PyPhi; tested for each optimal time series derived from (C)ARR.
- Assumption required by IIT/PyPhi; evaluated alongside Markov property for each candidate time series.
Methods (1)
method
- Strategy using GPT-4o, Claude 3.5 Sonnet, and Gemini to generate additional responses preserving original meaning, targeting ≥1000 words concatenated per score category.
Related by similarity (8)
cosine ≥ 0.65 · no typed edgeEntities in the same semantic neighborhood but without a typed relation to this one — candidates for new edges or unrecognized duplicates.
- Cognitive psychology finding that humans judge typicality based on whether an outcome looks representative of the generating process, leveraged to explain why VS recovers diversity
- The ideal limit of self-supervised learning as modeling the true conditional distribution.
- Cognitive tendency to judge frequency based on ease of recall, contributing to preference for typical responses in annotation
- Nielsen and Molich's method for finding UI flaws by applying usability heuristics.
- Question of how rationally agents query the world; subsumed by expected free energy minimization
- Selecting actions to maximize expected information gain.