Model-Based Machine Learning (MBML) - An Introductory Guide
The field of machine learning is teeming with numerous learning algorithms that researchers gravitate towards to resolve specific problems. Often their preferences hinge heavily on their familiarity with these systems. This conventional machine learning modus operandi obliges researchers to make certain assumptions to incorporate an existing algorithm. But MBML is changing that.
Model-based machine learning is an innovative methodology that creates a tailored solution for each novel problem. It bridges the gap for scientists, providing a centralized framework to develop a multitude of personalized models. This interpretative perspective emerged from a unique blend of three pivotal concepts - Factor graphs, Bayesian understanding, and Probabilistic Programming.
The pivotal philosophy behind MBML states that all assumptions regarding the problem domain should be explicitly stated in the model. Breaking it down, model-based deep learning is merely an array of assumptions articulated graphically.
Crucial Aspects of MBML
The pillar of MBML lies in the application of Probabilistic Graphical Models (PGMs), especially factor graphs. PGM acts as a graphical portrayal of the consolidated probability distribution of all random variables within a given model.
Factor graphs, a subtype of PGMs, use round and square nodes to denote variable probability distributions (factors) while edges exemplify conditional correlations between nodes. Factor graphs provide a comprehensive structure to simulate the joint dispersion of a random variable set. We harness Bayesian inference techniques to denote implicit parameters as random variables and uncover their probability circulations throughout the network.
The cornerstone enabling this novel machine learning structure is Bayesian inference. In MBML, hidden parameters are denoted as random variables with probability distributions allowing for a logical method to measure model parameter uncertainty. The classical machine learning counterpart assigns model variables average values, determined by optimizing an objective function. Bayesian inference, while intricate for large models with massive variables, is still feasible via Bayes' theorem, given the surge in computing power allowing for scalability to large datasets.
Probabilistic Programming (PP) is a progressive shift in computer science, developing languages equipped to compute uncertainty, in addition to logic. Today's languages can manage random variables, variable restraints, and inference packages. Now, using a PP language, you can succinctly express a model-based learning problem, automatically invoking an inference engine to generate problem-solving inference procedures.
Stages of MBML Development
The MBML development process follows three primary rules-based machine learning models:
- Model Depiction: Use factor graphs to detail the data creation process.
- Conditioning Data: Equip observed variables with their known values.
- Backward Reasoning: Use this to modernize the prior distribution of latent constructs, estimating the Bayesian probability distributions based on observable variables.