Random Weight Generator — Probability Weights & Traffic Splits

What are random weights used for?

Random weights are used in A/B and multivariate testing to assign traffic splits across test variants, in machine learning for initializing neural network parameters, in statistics for defining probability distributions over categories, in game development for loot drop rates, and in simulation to assign probabilities to different outcomes. Weights that sum to 1.0 form a valid probability distribution over a discrete set of events.

What is the Dirichlet distribution and how does it generate weights?

The Dirichlet distribution is the standard probability distribution over probability vectors. It takes a concentration parameter alpha. At alpha = 1, all distributions are equally likely. At alpha above 1, weights tend to be more evenly distributed. At alpha below 1, weights concentrate heavily on one or two items while others receive near-zero values. This tool implements three presets: balanced (alpha = 5), uniform (alpha = 1), and concentrated (alpha = 0.3) using exponential random variables as an approximation of Gamma(alpha, 1) sampling.

How do I split traffic evenly between test variants?

For a perfectly even split across N variants, each variant gets a weight of 1/N (or 100/N as a percentage). For 4 variants that is 25% each. This tool generates random splits when you want a realistic but non-uniform distribution, which is useful for testing analytics dashboards and simulating real-world traffic patterns where not all variants receive equal traffic.

What is the difference between weights and probabilities?

Probabilities are weights that sum to exactly 1.0. Raw weights can sum to any value and are normalized to produce probabilities. For example, weights [3, 1, 1] mean item A is three times as likely as B or C. Dividing each by the sum of 5 gives probabilities [0.6, 0.2, 0.2]. This tool outputs pre-normalized values that already sum to 1.0 or 100%, so they are ready to use directly as probabilities.

How are weights used in neural network initialization?

Neural network connection weights are initialized with small random values before training so each neuron learns to detect different features. Starting all weights at zero or the same value means all neurons compute identical outputs and receive identical gradient updates, causing them to always stay identical. Random initialization breaks this symmetry. Common strategies are He initialization (uniform random scaled by layer size) and Xavier initialization (normally distributed values).

What is the difference between gross weight, net weight, and tare weight?

Gross weight is the total weight including the product and all its packaging. Net weight is the weight of the product alone, without any packaging. Tare weight is the weight of the packaging itself (gross weight minus net weight). On food packaging, net weight is what must be displayed by law. In shipping, gross weight determines freight charges. Tare weight matters for bulk commodity trading where packaging is excluded from the price.

How do I convert between kilograms, pounds, and ounces?

1 kilogram = 2.20462 pounds. 1 pound = 0.453592 kilograms. 1 pound = 16 ounces. 1 kilogram = 35.274 ounces. 1 ounce = 28.3495 grams. To convert kg to lbs, multiply by 2.20462. To convert lbs to kg, multiply by 0.453592. The metric system uses base-10 increments (grams, kilograms, tonnes) while the imperial system uses historical units with arbitrary conversion factors.

What is a troy ounce and how is it different from a regular ounce?

A troy ounce (oz t) is the standard unit for weighing precious metals (gold, silver, platinum). One troy ounce equals 31.1035 grams. A regular avoirdupois ounce (used for everyday weights) equals 28.3495 grams — about 10% lighter. Gold and silver prices quoted per ounce always mean troy ounces. A troy pound is only 12 troy ounces (373.24g), lighter than a standard pound (453.59g).