The following is the problem I wish to solve. [[🎥estimator]] and [[generator]] 1. What: Solve non-identifiable problem with implicit prior knowledge - e.g. hierarchical ODE described [here](https://discourse.mc-stan.org/t/the-monster-model-hierarchical-ode-models/22683/12?u=hyunji.moon) i.e. the Monster problem 2. How: Find representation for structural knowledge via algorithmic convergence. Five tribes (symbolist, bayesian, connectionist, evolutionaries, analogizer) introduced in the book "[Master algorithm](https://en.wikipedia.org/wiki/The_Master_Algorithm)" will converge to the best form of knowledge represention such as the following. - As Bayesian posess explainable structure of distributional update with Bayes rule (i.e. change of measure), it is optimal to be used as a basic sauce (e.g. Bayesian deep learning, Bayesian reinforcement learning). - Connectionists, symbolists are being combined on top of Bayesian (as was predicted in the book). Analogizer (learning with kernel; Fourier?) and evolutionaries (algorithmic reasoning) are in line to be unified. 3. Why: To estimate parameter and predict states ![[Pasted image 20220619123843.png]] ![[Pasted image 20220619123916.png]] ![[Pasted image 20220619123932.png]]