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The network probability matrix describes the probability structure of a network based on the historical presence or absence of edges in a network. For
Network_probability_matrix
Random process independent of past history
process is characterized by a state space, a transition matrix describing the probabilities of particular transitions, and an initial state (or initial
Markov_chain
Academic field
in a social network. An alternate approach to network probability structures is the network probability matrix, which models the probability of edges occurring
Network_science
Nemenyi test Nested case-control study Nested sampling algorithm Network probability matrix Neutral vector Newcastle–Ottawa scale Newey–West estimator Newman–Keuls
List_of_statistics_articles
Matrix-valued random variable
In probability theory and mathematical physics, a random matrix is a matrix-valued random variable—that is, a matrix in which some or all of its entries
Random_matrix
Graph generated by a random process
vectors. The network probability matrix models random graphs through edge probabilities, which represent the probability p i , j {\displaystyle p_{i,j}}
Random_graph
Stochastic matrix representing links between entities
this is the adjacency matrix of links. A related matrix S corresponding to the transitions in a Markov chain of given network is constructed from A by
Google_matrix
Filling in missing entries of a matrix
reconstruction with high probability. In statistical learning point of view, the matrix completion problem is an application of matrix regularization which
Matrix_completion
Regularization method for artificial neural networks
vector matrix, and not only random weights P ( c ) {\displaystyle P(c)} – the probability c {\displaystyle c} to keep a row in the weight matrix w j {\displaystyle
Dropout_(neural_networks)
Commonly used representation of patterns in biological sequences
frequency matrix (PFM) is created by counting the occurrences of each nucleotide at each position. From the PFM, a position probability matrix (PPM) can
Position_weight_matrix
Notion in statistics
Fisher information matrix. The Fisher information matrix plays a role in an inequality like the isoperimetric inequality. Of all probability distributions
Fisher_information
Description of limiting behavior in probabilistic algorithms
mathematics, an event that occurs with high probability (often shortened to w.h.p. or WHP) is one whose probability depends on a certain number n and goes
With_high_probability
Measure of network community structure
community 2, s v = − 1 {\displaystyle s_{v}=-1} . Let the adjacency matrix for the network be represented by A {\displaystyle A} , where A v w = 0 {\displaystyle
Modularity_(networks)
Metric used to rank web pages
a maximal real eigenvalue of the Google matrix G ∗ {\displaystyle G^{*}} constructed for a directed network with the inverted directions of links. It
CheiRank
Algorithm for modelling sequential data
weight matrix for further processing depending on the input. One of its two networks has "fast weights" or "dynamic links" (1981). A slow neural network learns
Transformer_(deep_learning)
Probability concept
variable and then move to a different state as specified by the probabilities of a stochastic matrix. An equivalent formulation describes the process as changing
Continuous-time_Markov_chain
Bioinformatics tool
how meaningful it is. This requires a scoring matrix, or a table of values that describes the probability of a biologically meaningful amino-acid or nucleotide
BLOSUM
Matrix representation of a graph
theory, the Laplacian matrix, also called the graph Laplacian, admittance matrix, Kirchhoff matrix, or discrete Laplacian, is a matrix representation of a
Laplacian_matrix
matrix of exposure probabilities for each unit in the analysis. First, define a diagonal matrix with a vector of treatment assignment probabilities P
Spillover_(experiment)
Mathematical wave functions
the study of many-body quantum systems and fluids. Tensor networks extend one-dimensional matrix product states to higher dimensions while preserving some
Tensor_network
Concept in science
The probability of the outcome of an experiment is never negative, although a quasiprobability distribution allows a negative probability, or quasiprobability
Negative_probability
Set of random variables
In the domain of physics and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having
Markov_random_field
Integer matrices with +1 or −1 determinant; invertible over the integers. GL_n(Z)
mathematics, a unimodular matrix M is a square integer matrix having determinant +1 or −1. Equivalently, it is an integer matrix that is invertible over
Unimodular_matrix
Equations governing time evolution of physical systems
determined by a transition rate matrix. The equations are a set of differential equations – over time – of the probabilities that the system occupies each
Master_equation
Class of artificial neural network
Ising–Lenz–Little model) is a generative stochastic artificial neural network that can learn a probability distribution over its set of inputs. RBMs were initially
Restricted_Boltzmann_machine
Probability distribution
In probability theory and statistics, the beta distribution is a family of continuous probability distributions defined on the interval [0, 1] or (0, 1)
Beta_distribution
Smooth approximation of one-hot arg max
as the last activation function of a neural network to normalize the output of a network to a probability distribution over predicted output classes.
Softmax_function
Mathematical discipline
mathematical theory of probability, a Jackson network (sometimes called a Jacksonian network) is a class of queueing networks where the equilibrium distribution
Jackson_network
Array of numbers
random numbers, subject to suitable probability distributions, such as matrix normal distribution. Beyond probability theory, they are applied in domains
Matrix_(mathematics)
Algorithms for matrix decomposition
Non-negative matrix factorization (NMF or NNMF), also non-negative matrix approximation is a group of algorithms in multivariate analysis and linear algebra
Non-negative matrix factorization
Non-negative_matrix_factorization
Two closely related models for generating random graphs
Statistical models for network analysisPages displaying short descriptions of redirect targets describe a general probability distribution of graphs on
Erdős–Rényi_model
Type of artificial neural network
representations for higher capsules. The output is a vector consisting of the probability of an observation, and a pose for that observation. This vector is similar
Capsule_neural_network
Concept in network science
communities; a symmetric r × r {\displaystyle r\times r} matrix P {\displaystyle P} of edge probabilities. The edge set is then sampled at random as follows:
Stochastic_block_model
Technique in information theory
P {\displaystyle P\,} as a Markov state transition probability matrix, the vector of probabilities of the 'states' after t {\displaystyle t\,} steps,
Information_bottleneck_method
Exponentially decreasing bounds on tail distributions of random variables
In probability theory, a Chernoff bound is an exponentially decreasing upper bound on the tail of a random variable based on its moment generating function
Chernoff_bound
Fourier transform of the probability density function
In probability theory and statistics, the characteristic function of any real-valued random variable completely defines its probability distribution. If
Characteristic function (probability theory)
Characteristic_function_(probability_theory)
statistics and probability theory. Bernoulli matrix — a square matrix with entries +1, −1, with equal probability of each. Centering matrix — a matrix which,
List_of_named_matrices
Measure of connection disorder in a network
aren't invariant to the chosen network description. The Shannon entropy can be measured for the network degree probability distribution as an average measurement
Network_entropy
Generalization of gamma distribution to multiple dimensions
distribution in 1928. Other names include Wishart ensemble (in random matrix theory, probability distributions over matrices are usually called "ensembles"), or
Wishart_distribution
Paradigm in machine learning that uses no classification labels
expressed as a low probability that the erroneous output occurs, or it might be expressed as an unstable high energy state in the network. In contrast to
Unsupervised_learning
Stochastic point process in mathematics
arise as important tools in random matrix theory, combinatorics, physics, machine learning, and wireless network modeling. Consider some positively charged
Determinantal_point_process
Algorithm in mathematics
{\displaystyle S_{1}} transition probabilities and normalize each row of the transition matrix so that the probabilities of transitions from a given starting
Baum–Welch_algorithm
Statistical Markov model
1. Thus, the N × N matrix of transition probabilities is a Markov matrix. Because any transition probability can be determined once the others are known
Hidden_Markov_model
Method of analysis in probability theory
In probability theory, the matrix geometric method is a method for the analysis of quasi-birth–death processes, continuous-time Markov chain whose transition
Matrix_geometric_method
Matrix representing the frequency of evolution of a protein or nucleotide sequence
PAM1 matrix, and multiple substitutions can occur at the same site. With this assumption, the PAM2 matrix can estimated by squaring the probabilities. Using
Substitution_matrix
state of network is x {\displaystyle x} , node i and node j interact with each other, and one of them will change its state. The transition matrix depends
Rumor spread in social network
Rumor_spread_in_social_network
Probabilistic Condorcet method
) {\displaystyle {\begin{matrix}{\begin{matrix}&&a\quad &b\quad &c\quad \\\end{matrix}}\\{\begin{matrix}a\\b\\c\\\end{matrix}}{\begin{pmatrix}0&1&-1\
Maximal_lotteries
_{m\in t_{j}}p(n',m),} where p(i,j) is the probability of moving from state i to state j. Consider the matrix P = ( 1 2 3 8 1 16 1 16 7 16 7 16 0 1 8 1
Lumpability
Probabilistic classification algorithm
uncertainty (with naive Bayes models often producing wildly overconfident probabilities). However, they are highly scalable, requiring only one parameter for
Naive_Bayes_classifier
lists articles related to probability theory. In particular, it lists many articles corresponding to specific probability distributions. Such articles
Catalog of articles in probability theory
Catalog_of_articles_in_probability_theory
Underground fungal networks that connect individual plants together
A mycorrhizal network (also known as a common mycorrhizal network or CMN) is an underground network found in forests and other plant communities, created
Mycorrhizal_network
Real-valued function that quantifies similarity between two objects
similarity function Self-similarity matrix Semantic similarity – Concept in natural language processing Similarity (network science) Similarity (philosophy) –
Similarity_measure
Topics referred to by the same term
Computer Algebra System (CAS) Singular matrix, a matrix that is not invertible Singular measure, a measure or probability distribution whose support has zero
Singular
Technique for the generative modeling of a continuous probability distribution
2015 as a method to train a model that can sample from a highly complex probability distribution. They used techniques from non-equilibrium thermodynamics
Diffusion_model
Reputation management algorithm for peer-to-peer networks
If we assume that a user knew the cij values for the whole network in the form of a matrix C, then trust vector t ¯ i {\displaystyle {\bar {t}}_{i}} that
EigenTrust
Machine learning algorithm
different input feature. Each leaf of the tree is labeled with a class or a probability distribution over the classes, signifying that the data set has been
Decision_tree_learning
Mathematical theory on behavior of connected clusters in a random graph
network? By Kolmogorov's zero–one law, for any given p, the probability that an infinite cluster exists is either zero or one. Since this probability
Percolation_theory
Algorithm used by Google Search to rank web pages
the matrix M {\displaystyle {\mathcal {M}}} is a transition probability, i.e., column-stochastic and R {\displaystyle \mathbf {R} } is a probability distribution
PageRank
Machine learning technique
linear operations in high-dimensional space by operations on the kernel matrix: K X := [ k ( x i , x j ) ] i , j ∈ 1 : N {\displaystyle K_{X}:=[k(x_{i}
Random_feature
Equations describing traffic rate
the other nodes on the network. If external arrivals at node i have rate γ i {\displaystyle \gamma _{i}} , and the routing matrix is P, the traffic equations
Traffic_equations
Class of variational quantum states
generated such that they are uniformly distributed according to the Born probability density P ( S ) ∝ | F ( s 1 … s N ; W ) | 2 {\displaystyle P(S)\propto
Neural_network_quantum_states
Theorem in linear algebra
In matrix theory, the Perron–Frobenius theorem, proved in its first part by Oskar Perron (1907) and extended by Georg Frobenius (1912), asserts that a
Perron–Frobenius_theorem
Distribution over functions corresponding to an infinitely wide Bayesian neural network
distinguished by how it is obtained. Bayesian networks are a modeling tool for assigning probabilities to events, and thereby characterizing the uncertainty
Neural network Gaussian process
Neural_network_Gaussian_process
Inference algorithm for hidden Markov models
continuous and discrete probability models. We transform the probability distributions related to a given hidden Markov model into matrix notation as follows
Forward–backward_algorithm
Machine learning technique
Networks that perform verbatim translation without regard to word order would show the highest scores along the (dominant) diagonal of the matrix. The
Attention_(machine_learning)
sparseness, a network visualization is an appropriate way to represent this dataset. A network representation of the proximity matrix helps to develop
The_Product_Space
Deep learning method
evolutionary arms race between both networks. The original GAN is defined as the following game: Each probability space ( Ω , μ ref ) {\displaystyle (\Omega
Generative adversarial network
Generative_adversarial_network
Architectural motif in neural networks for aggregating information
LayerNorm-feedforward-softmax module into a probability distribution, which is the network's prediction of class probability distribution. This is the one used
Pooling_layer
Networks with multiple kinds of relations
multidimensional network with D {\displaystyle D} dimensions, the adjacency matrix becomes a multilayer adjacency tensor, a four-dimensional matrix of size (
Multidimensional_network
Generalization of the concept from statistical mechanics
The partition function or configuration integral, as used in probability theory, information theory and dynamical systems, is a generalization of the
Partition function (mathematics)
Partition_function_(mathematics)
Interpretation of probability
Bayesian probability (/ˈbeɪziən/ BAY-zee-ən or /ˈbeɪʒən/ BAY-zhən) is an interpretation of the concept of probability, in which, instead of frequency or
Bayesian_probability
Signal processing computational method
matrix with a vector. Signal mixtures tend to have Gaussian probability density functions, and source signals tend to have non-Gaussian probability density
Independent component analysis
Independent_component_analysis
Model-free reinforcement learning algorithm
Q-Network (DQN), by using the trust region method to limit the KL divergence between the old and new policies. However, TRPO uses the Hessian matrix (a
Proximal_policy_optimization
Environmental impact assessment method
The Leopold matrix is a qualitative environmental impact assessment method developed in 1971 by Luna Leopold and collaborators for the USGS. It is used
Leopold_matrix
Classification of Artificial Neural Networks (ANNs)
computation are often used to optimize the weight matrix. The Hopfield network (like similar attractor-based networks) is of historic interest although it is not
Types of artificial neural networks
Types_of_artificial_neural_networks
Mathematical study of waiting lines, or queues
Traffic jam Traffic generation model Flow network Sundarapandian, V. (2009). "7. Queueing Theory". Probability, Statistics and Queueing Theory. PHI Learning
Queueing_theory
Hidden Markov model algorithm
directed graphs of variables (see sum-product networks). For an HMM such as this one: this probability is written as p ( x t | y 1 : t ) {\displaystyle
Forward_algorithm
2010-11-17. "Loss networks". Frank Kelly. Retrieved 2010-11-17. Kelly, F. P. (1991). "Loss Networks". The Annals of Applied Probability. 1 (3): 319. doi:10
Loss_network
Basic circuit in quantum computing
logic Quantum memory Quantum network Quantum Zeno effect Reversible computing Unitary transformation (quantum mechanics) Matrix multiplication of quantum
Quantum_logic_gate
Type of artificial neural network
the conditional probability of y given x {\displaystyle \mathbf {x} } . The conditional probability is related to the joint probability through Bayes'
Radial_basis_function_network
Within mathematics, an N×N Euclidean random matrix  is defined with the help of an arbitrary deterministic function f(r, r′) and of N points {ri} randomly
Euclidean_random_matrix
Restricted model of non-universal quantum computation
"hide" the above probability p ( t 1 , t 2 , . . . , t N ) {\displaystyle p(t_{1},t_{2},...,t_{N})} into an N×N random unitary matrix. This can be done
Boson_sampling
Family of random graph models
preserve it only in expectation. These models define probability distributions over the edges of the network. Canonical configuration models are often referred
Configuration_model
Overview of and topical guide to machine learning
of experts Multiple kernel learning Naive Bayes classifier Non-negative matrix factorization Online machine learning Out-of-bag error Prefrontal cortex
Outline_of_machine_learning
Analysis of potential system failures
be needed to determine exact probability and risk levels. Preliminary risk levels can be selected based on a risk matrix like that shown below, based
Failure mode and effects analysis
Failure_mode_and_effects_analysis
Continuous probability distribution
In probability theory and statistics, the logistic distribution is a continuous probability distribution. Its cumulative distribution function is the logistic
Logistic_distribution
Area of discrete mathematics
the given adjacency matrix. It also focuses on the Laplacian matrix of a graph, which involves the degree matrix (a diagonal matrix that represents the
Graph_theory
Generative adversarial network variant
with 2 players: generator and discriminator. The game is defined over a probability space ( Ω , B , μ r e f ) {\displaystyle (\Omega ,{\mathcal {B}},\mu
Wasserstein_GAN
Wiener process with reflecting spatial boundaries
In probability theory, reflected Brownian motion (or regulated Brownian motion, both with the acronym RBM) is a Wiener process in a space with reflecting
Reflected_Brownian_motion
Measure of similarity and diversity between sets
There is also a version of the Jaccard distance for measures, including probability measures. If μ {\displaystyle \mu } is a measure on a measurable space
Jaccard_index
Russian mathematician (1856–1922)
information source Markov network Markov number Markov property Markov process Stochastic matrix (also known as Markov matrix) Subjunctive possibility
Andrey_Markov
In queueing theory, a discipline within the mathematical theory of probability, a fluid queue (fluid model, fluid flow model or stochastic fluid model)
Fluid_queue
Type of stochastic recurrent neural network
source of the logistic function found in probability expressions in variants of the Boltzmann machine. The network runs by repeatedly choosing a unit and
Boltzmann_machine
Degree of connectedness within a graph
between other humans in a social network by Linton Freeman. In his conception, vertices that have a high probability to occur on a randomly chosen shortest
Centrality
Finds likely sequence of hidden states
states input init: initial probabilities of each state input trans: S × S transition matrix input emit: S × M emission matrix input obs: sequence of T observations
Viterbi_algorithm
Computational model used in machine learning
network is a generative model that models a probability distribution over output patterns. The second network learns by gradient descent to predict the
Neural network (machine learning)
Neural_network_(machine_learning)
Study of graphs as a representation of relations between discrete objects
expected random probabilities, they are said to be neutral. There are three methods to quantify degree correlations. The recurrence matrix of a recurrence
Network_theory
similarly. N-dimensional Gaussian probability density function with random variable vector x, mean vector μ and covariance matrix Σ is W ( x , μ , Σ ) = 1 (
Gaussian_network_model
Probability distribution
{S^{0}} ,} for all x > 0, where exp( · ) is the matrix exponential. It is usually assumed the probability of process starting in the absorbing state is
Phase-type_distribution
Tool for working with matrices
procedure can compute the probabilities such that each agent, looking at the matrix of probabilities, prefers his row of probabilities over the rows of all
Birkhoff_algorithm
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
Surname or Lastname
English
English : habitational name from Newark in Cambridgeshire or Newark on Trent in Nottinghamshire, both named from Old English nīwe ‘new’ + weorc ‘fortification’, ‘building’.
Surname or Lastname
English
English : in all probability an English variant of Scottish Lachlan (see McLachlan), altered through folk etymology. However, Black cites one John sine terra (c. 1180–1214), suggesting that the surname could have arisen quite literally as a nickname for a man with no land.
Boy/Male
Indian, Sanskrit
Network of Roots; The Ocean
Surname or Lastname
English
English : variant of Fretter, an occupational name for a maker of ornaments (especially for the hair) consisting of jewels set in a lattice network, from an agent derivative of Middle English frette, Old French frete ‘interlaced work’.
Surname or Lastname
English (Yorkshire)
English (Yorkshire) : in all probability from the Swale river in Yorkshire. (Reaney and Wilson list a 17th-century example, Swayles, with this origin.) Alternatively, it may be a metronymic from the Old Norse female personal name Svala.
Girl/Female
Assamese, Bengali, Celebrity, Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Sanskrit, Sindhi, Tamil, Telugu, Traditional
Line; Artwork; Beauty; The Heart of God; Limit
Surname or Lastname
English (London)
English (London) : patronymic from the personal name Piers (see Pierce).North German : patronymic from the personal name Pier, a variant of Peer, reduced form of Peter.Born in Yorkshire, England, Abraham Pierson (1609–78) was the first pastor of the settlements at Southampton, Long Island, NY; Branford, CT, and Newark, NJ. He left his library of more than 400 books, one of the most extensive in the colonies, to his son Abraham, who was one of the first trustees of Yale College.
Girl/Female
Hindu, Indian
Artwork Like Moon
Girl/Female
Gujarati, Hindu, Indian, Kannada
God's Artwork; Beautiful Art; God's Grace
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
Boy/Male
Anglo, British, English
God
Male
French
Old French form of German Harmand, ARMAND means "bold/hardy man."
Boy/Male
English Anglo Saxon
Wild boar.
Boy/Male
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Mythological, Oriya, Sanskrit, Tamil, Telugu
Lord Shiva
Girl/Female
Assamese, Hindu, Indian, Tamil
Restless; Moon
Girl/Female
Basque
End.
Boy/Male
Gujarati, Hindu, Indian, Kannada, Malayalam, Marathi, Tamil, Telugu
Making Alive
Girl/Female
Gujarati, Hindu, Indian, Kannada
Goddess Saraswathi / Lakshmi
Boy/Male
Muslim
Visible
Boy/Male
Hindu
Worldly life
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
NETWORK PROBABILITY-MATRIX
n.
Probability; likelihood.
n.
Likelihood of the occurrence of any event in the doctrine of chances, or the ratio of the number of favorable chances to the whole number of chances, favorable and unfavorable. See 1st Chance, n., 5.
n.
The doctrine of the probabilists.
n.
One who maintains that certainty is impossible, and that probability alone is to govern our faith and actions.
n.
Probability; verisimilitude.
a.
Like network; complicated.
n.
The quality or state of being probable; appearance of reality or truth; reasonable ground of presumption; likelihood.
n.
That which is or appears probable; anything that has the appearance of reality or truth.
pl.
of Probability
superl.
Having probability; affording probability; probable; likely.
n.
Probability.
n.
A net or network; a plexus; particularly, a network of blood vessels or nerves, or a part resembling a network.
adv.
In all probability; probably.
a.
Resembling network; retiform.
n.
A fabric of threads, cords, or wires crossing each other at certain intervals, and knotted or secured at the crossings, thus leaving spaces or meshes between them.
n.
Likelihood; probability.
pl.
of Improbability
n.
Any system of lines or channels interlacing or crossing like the fabric of a net; as, a network of veins; a network of railroads.
n.
Probability.
n.
One who maintains that a man may do that which has a probability of being right, or which is inculcated by teachers of authority, although other opinions may seem to him still more probable.