Search results
Results from the WOW.Com Content Network
Algorithmic inference gathers new developments in the statistical inference methods made feasible by the powerful computing devices widely available to any data analyst. Cornerstones in this field are computational learning theory , granular computing , bioinformatics , and, long ago, structural probability ( Fraser 1966 ).
The algorithms used by programs like compilers are equivalent to the informally structured reasoning above, but a bit more verbose and methodical. The exact details depend on the inference algorithm chosen (see the following section for the best-known algorithm), but the example below gives the general idea.
Variable elimination (VE) is a simple and general exact inference algorithm in probabilistic graphical models, such as Bayesian networks and Markov random fields. [1] It can be used for inference of maximum a posteriori (MAP) state or estimation of conditional or marginal distributions over a subset of variables.
Researchers at MIT are digging into this issue and have created an algorithm that can help optimize how fresh the data is on a simple wireless network. MIT algorithm ensures drones get the ...
An article of Simon Tavaré and co-authors was first to propose an ABC algorithm for posterior inference. [7] In their seminal work, inference about the genealogy of DNA sequence data was considered, and in particular the problem of deciding the posterior distribution of the time to the most recent common ancestor of the sampled individuals ...
The Baum–Welch algorithm, the primary method for inference in hidden Markov models, is numerically unstable due to its recursive calculation of joint probabilities. As the number of variables grows, these joint probabilities become increasingly small, leading to the forward recursions rapidly approaching values below machine precision.
A separate inference engine processes rules and adds, deletes, or modifies a knowledge store. Forward chaining inference engines are the most common, and are seen in CLIPS and OPS5. Backward chaining occurs in Prolog, where a more limited logical representation is used, Horn Clauses. Pattern-matching, specifically unification, is used in Prolog.
Grammar induction (or grammatical inference) [1] is the process in machine learning of learning a formal grammar (usually as a collection of re-write rules or productions or alternatively as a finite-state machine or automaton of some kind) from a set of observations, thus constructing a model which accounts for the characteristics of the observed objects.