the weights of elastic ties introduce
How strong need weak interactions (neighbour's neighbours) to be pooled to ensure a dynamic capacity for exogeneous shocks?
Using square positive definite matrices, can we find a sparse graph that indicate conditional dependence among the domain of a priori independent covariates estimating the maximum likelihood of potential interactions with or without a target matrix that restrict the search space to predefined characteristics of likely interactions, like just a diagonal, meaning having pairwise interactions and additive decomposition.
There is a weak correspondence between likelihood, estimated parameter variance and regularization to care less about multiplicative effects that could deform the shape of the parameter or explain non-linear interactions, i.e. more least squares. This is especially crucial for highdimensional data, where feature selection must be done carefully (as heavy tails can lie at the edge of stability, thus limitting performance gains (like just changes in exploration strategies in expectation maximization mode, but also "perturbation management" (super heavy tailed risk of exploitative adversarial probes) to mean field dynamics due to risk bifurcation), because it might kill a species that would otherwise outperform the arena. Therefore finding a balanced trade-off between L1 and L2 regularization is critical to understand a dynamic program and its different modes it can run efficiently on with different work load and queueing scenarios
Effectively, using a generative splitting field and any sampling strategy, we can use another search heuristic in parallel to actively compare different hypothesises iteratively, how known parameters are distributed and whether different utilisations infer systematic biases that could recover the exact data distribution by tracing back combinatorial results which rule out anomalies otherwise (one of which is another lost (far more feature rich) draft due to confounding environmental maladaptation).
The main problem of this approach is that not all data can be recovered efficiently, simply because some likelihood models are nonsense and testing them only converges weakly. This suggests that certain network structures of parameter distributions are more likely to have better recovery rates because they outscale the network flow that support a dense hyperparameter structure, decreasing thresholds to increase elasticity of edge capacity, while increasing the stability in sparse regions by lowering the chances to generate false positives (via injecting wrong DAGs) and thus the variability of the model, increasing its resilience. Usually, such a structure yields a multivariate dependency model with differently interacting equivalence or preference relations, all of which are american dreams and should be essential up to some degree.
Usually model selection routines stage a variety of candidate hyperparameter shapes which could also help developing the arena in which they're getting reinforced to stabilize the current paradigm. One should forget the possibility to run this process on a computer and rather start a philosophical discussion on the reasonability of such a business. Though, we can learn a lot about how we can live our lifes in a society supporting each other and figure out how to build up for the people AND care about the environment we're leaving. But in the end, to incentivize learning from experiences is no sustainable game we should play (unprotectedly) outside in the real world, nor should such a structure dominate our society. This is a process that emerges on its own under bad model assumptions and should on be an incentive to do better out of oneself. We don't need virtue signaling and synthetic noise to boost our economy, this is a cause of frustration, reproducing non-archimedian spillover to exploit weaknesses of parametric discontinuations by reinforcing evolution as a gamified big filter (let's call that 'slow play fascism')
tbc..