Conditioning
Conditioning is restricting or guiding a probabilistic or generative process using specified information, variables, values, or events.
Bayesian Optimization in Action
Conditioning is the mathematical operation of updating a probability distribution after fixing or observing the value of one or more variables.
- How it is used
- Updates beliefs about unobserved variables after incorporating observed values in multivariate normal distributions and Gaussian processes.
- How deep it goes
- Defines the operation and explains posterior behavior through Gaussian examples, but defers the math-heavy derivation.
- Examples
- Uses correlated price variables, observations B = 4 or C = 4, parallel-coordinate plots, and a knot analogy.
- What you can do after
- Evaluate how observations shift posterior means and reduce uncertainty in Gaussian models.
Read how Bayesian Optimization in Action explains conditioning →
Build a Text-to-Image Generator (from Scratch)
Conditioning information is auxiliary input supplied to a generative process to influence the characteristics of its output.
- How it is used
- Steers diffusion-based image generation toward class labels or text prompts while balancing alignment and diversity.
- How deep it goes
- Explains conditional and unconditional training, prediction combination, CFG scale, and the resulting quality-diversity trade-off.
- Examples
- Uses class labels, text prompts, partially denoised samples, and conditional-versus-unconditional predictions as concrete examples.
- What you can do after
- Apply classifier-free guidance to control how closely generated images match conditioning information.
Read how Build a Text-to-Image Generator (from Scratch) explains conditioning →
Related concepts
- conditional distributionproduces · A conditional distribution is the probability distribution of a random quantity given specified information about other quantities or conditions.
- backdoor pathaddresses · A backdoor path is a path between a putative cause and an outcome that begins with an arrow into the cause and can create a noncausal association.
- collideruses · A collider is a variable in a causal graph that receives directed edges from at least two other variables, potentially inducing associations when conditioned on.
- confounderaddresses · A confounder is a variable associated with both an explanatory or treatment variable and an outcome, whose influence can distort their apparent relationship or estimated causal effect.
- independenceused by · Two events or random quantities are independent when knowing the outcome or value of one does not change the probability or distribution of the other.
- interventioncontrasts with · An intervention is a deliberate, externally imposed change or control applied to a variable or system to alter or assess its behavior or outcomes.
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