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v. t. e. In probability theory, a tree diagram may be used to represent a probability space. A tree diagram may represent a series of independent events (such as a set of coin flips) or conditional probabilities (such as drawing cards from a deck, without replacing the cards). [1] Each node on the diagram represents an event and is associated ...
Client-side Javascript SVG viewer for annotated rooted trees. Also supports phylogenetic networks. Iroki [5] Automatic customization and visualization of phylogenetic trees. iTOL - interactive Tree Of Life [6] annotate trees with various types of data and export to various graphical formats; scriptable through a batch interface. Microreact [7]
A decision tree is a flowchart -like structure in which each internal node represents a "test" on an attribute (e.g. whether a coin flip comes up heads or tails), each branch represents the outcome of the test, and each leaf node represents a class label (decision taken after computing all attributes). The paths from root to leaf represent ...
Event tree analysis (ETA) is a forward, top-down, logical modeling technique for both success and failure that explores responses through a single initiating event and lays a path for assessing probabilities of the outcomes and overall system analysis. [1] This analysis technique is used to analyze the effects of functioning or failed systems ...
Monte Carlo tree search. In computer science, Monte Carlo tree search (MCTS) is a heuristic search algorithm for some kinds of decision processes, most notably those employed in software that plays board games. In that context MCTS is used to solve the game tree. MCTS was combined with neural networks in 2016 [1] and has been used in multiple ...
Probability theory. In probability and statistics, a Bernoulli process (named after Jacob Bernoulli) is a finite or infinite sequence of binary random variables, so it is a discrete-time stochastic process that takes only two values, canonically 0 and 1. The component Bernoulli variables Xi are identically distributed and independent.
In a perfect information scenario, E can be defined as the sum product of the probability of a good outcome g times its cost k, plus the probability of a bad outcome (1-g) times its cost k'>k: E = gk + (1-g)k', which is revised to reflect expected cost F of perfect information including consulting cost c. The perfect information case assumes ...
The information stored per node in the randomized binary tree is simpler than in a treap (a small integer rather than a high-precision random number), but it makes a greater number of calls to the random number generator (O(log n) calls per insertion or deletion rather than one call per insertion) and the insertion procedure is slightly more ...