Probabilistic Computing
Unlocking the Power of Uncertainty with Deterministic Probabilistic Computing
- Built To Order
- Production Ready
Probabilistic inference algorithms are designed to compute the probabilities of events or variables under uncertainty. This process works in reverse—starting from observed data and inferring the hidden or latent variables that explain those observations.
However, this inference is computationally challenging and often exponentially hard. Traditional approaches have relied on techniques such as Markov Chain Monte Carlo (MCMC), Variational Inference, and Expectation-Maximization to approximate these probabilities.
Automatski offers a deterministic approach to probabilistic inference. It overcomes the inherent complexity of probabilistic computing by providing a robust solution that effectively eliminates the traditional challenges faced in implementing probabilistic algorithms and inference methods.
With Automatski, the entire class of problems associated with probabilistic computing and inference is resolved, enabling reliable and efficient handling of uncertainty in computational systems.