💡 Words with a Similar Meaning to "Markov logic network"
Found via reverse dictionary — words that share a conceptual meaning.
| Word | Definition |
|---|---|
| markov modelnoun | A stochastic model for randomly changing systems, where it is assumed that future states depend only on the current state and not those that preceded it. |
| markov chainnoun | (probability theory) A discrete-time stochastic process containing a Markov property. |
| neural network gaussian process | A Neural Network Gaussian Process is a Gaussian process obtained as the limit of a certain type of sequence of neural networks. |
| markov random field | In the domain of physics and probability, a Markov random field, Markov network or undirected graphical model is a set of random variables having a Markov property described by an undirected graph. |
| quantum markov chain | In mathematics, the quantum Markov chain is a reformulation of the ideas of a classical Markov chain, replacing the classical definitions of probability with quantum probability. |
| markov decision process | Markov decision process, also called a stochastic dynamic program or stochastic control problem, is a model for sequential decision making when outcomes are uncertain. |
| probabilistic programming | a programming paradigm in which probabilistic models are specified and inference for these models is performed automatically. |
| probabilistic logic | Probabilistic logic (also probability logic and probabilistic reasoning) involves the use of probability and logic to deal with uncertain situations. |
| probabilistic neural network | A probabilistic neural network is a feedforward neural network, which is widely used in classification and pattern recognition problems. |
| hidden markov modelnoun | A statistical Markov model in which the system being modelled is assumed to be a Markov process with unobserved (hidden) states. |
| markov chain monte carlo | In statistics, Markov chain Monte Carlo is a class of algorithms used to draw samples from a probability distribution. |
| markov kernel | In probability theory, a (also known as a stochastic kernel or probability kernel) a map that in the general theory of Markov processes plays the role that the transition matrix does in the theory of Markov processes with a finite state space. |
| bayesian networknoun | (statistics) A directed acyclic graph whose vertices represent random variables and whose directed edges represent conditional dependencies. Each random variable can fall into any of at least two mutually disjoint states, and has a probability function which takes as inputs the states of its parent nodes and returns as output the probability of being in a certain state for a given combination of the states of its parent nodes. A node without parent nodes just has an unconditioned probability of being in some given state. |
| restricted boltzmann machine | A restricted Boltzmann machine (also called a restricted Sherrington–Kirkpatrick model with external field or restricted stochastic Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. |
| quantum finite automaton | In quantum computing, quantum finite automata or quantum state machines are a quantum analog of probabilistic automata or a Markov decision process. |
| dynamic bayesian network | A dynamic Bayesian network is a Bayesian network which relates variables to each other over adjacent time steps. |
| balance equation | In probability theory, a balance equation is an equation that describes the probability flux associated with a Markov chain in and out of states or set of states. |
| markov propertynoun | (probability theory) The memoryless property of Markov models, according to which the future states depend only on the current state and not those that preceded it. |
| graphical model | A graphical model or probabilistic graphical model or structured probabilistic model is a probabilistic model for which a graph expresses the conditional dependence structure between random variables. |
| bayesian inference | (or ) a method of statistical inference in which Bayes' theorem is used to calculate a probability of a hypothesis, given prior evidence, and update it as more information becomes available. |
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