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information retrieval, divergence from randomness (DFR) is a generalization of one of the very first models, Harter's 2-Poisson indexing-model. It is one type
Divergence-from-randomness model
Divergence-from-randomness_model
Topics referred to by the same term
league for Football in England Harter's 2-Poisson indexing-model a divergence from randomness model used in information retrieval PLL (disambiguation) PL (disambiguation)
PL2_(disambiguation)
Finding information for an information need
Uncertain inference Language models Divergence-from-randomness model Latent Dirichlet allocation Feature-based retrieval models view documents as vectors
Information_retrieval
Mathematical statistics distance measure
mathematical statistics, the Kullback–Leibler (KL) divergence (also called relative entropy and I-divergence), denoted D KL ( P ∥ Q ) {\displaystyle D_{\text{KL}}(P\parallel
Kullback–Leibler_divergence
Topics referred to by the same term
organisation in Germany Dihydroflavonol 4-reductase, an enzyme class Divergence-from-randomness model, in information retrieval Dounreay Fast Reactor, Scotland Dual
DFR
Collection of statistical models
when applied to data from non-randomized experiments or observational studies, model-based analysis lacks the warrant of randomization. For observational
Analysis_of_variance
Apparent lack of pattern or predictability in events
as often as 4. In this view, randomness is not haphazardness; it is a measure of uncertainty of an outcome. Randomness applies to concepts of chance
Randomness
Measure of difference between two points
class of divergences. When the points are interpreted as probability distributions – notably as either values of the parameter of a parametric model or as
Bregman_divergence
Technique for the generative modeling of a continuous probability distribution
making biased random steps that are a sum of pure randomness (like a Brownian walker) and gradient descent down the potential well. The randomness is necessary:
Diffusion_model
Concept in information theory
the min-entropy is used in the context of randomness extractors. Let X {\displaystyle X} be a discrete random variable with possible outcomes in the set
Rényi_entropy
Function that measures dissimilarity between two probability distributions
information geometry, a divergence is a kind of statistical distance: a binary function which establishes the separation from one probability distribution
Divergence_(statistics)
Type of mathematical model
statistical model is a mathematical model that embodies a set of statistical assumptions concerning the generation of sample data (and similar data from a larger
Statistical_model
Interactions among inertial, elastic, and aerodynamic forces
simple models (e.g. single aileron on an Euler-Bernoulli beam), control reversal speeds can be derived analytically as for torsional divergence. Control
Aeroelasticity
Machine learning technique
the KL divergence (a measure of statistical distance between distributions) between the model being fine-tuned and the initial supervised model. By choosing
Reinforcement learning from human feedback
Reinforcement_learning_from_human_feedback
Description of the behaviour of bosons
information retrieval. The method is one of a collection of DFR ("Divergence From Randomness") models, the basic notion being that Bose–Einstein statistics may
Bose–Einstein_statistics
Distance between two statistical objects
pseudometrics on distributions Kullback–Leibler divergence Rényi divergence Jensen–Shannon divergence Ball divergence Bhattacharyya distance (despite its name
Statistical_distance
Mathematical formalization of card shuffling
cards, the Gilbert–Shannon–Reeds model describes the probabilities obtained from a certain mathematical model of randomly cutting and then riffling a deck
Gilbert–Shannon–Reeds_model
Quantifying marketing influence
comparing MTA outputs to results from randomized experiments have found substantial discrepancies, with attribution models systematically misallocating credit
Attribution_(marketing)
Variable representing a random phenomenon
object which depends on random events. The term 'random variable' in its mathematical definition refers to neither randomness nor variability but instead
Random_variable
Probabilistic model
graph expresses the conditional dependence structure between random variables. Graphical models are commonly used in probability theory, statistics—particularly
Graphical_model
Generalization of the one-dimensional normal distribution to higher dimensions
vector space, and the result has units of nats. The Kullback–Leibler divergence from N 1 ( μ 1 , Σ 1 ) {\displaystyle {\mathcal {N}}_{1}({\boldsymbol {\mu
Multivariate normal distribution
Multivariate_normal_distribution
Probability distribution
The directed Kullback–Leibler divergence in nats of e λ {\displaystyle e^{\lambda }} ("approximating" distribution) from e λ 0 {\displaystyle e^{\lambda
Exponential_distribution
Algorithm for modelling sequential data
needing double the amount of embedding-related parameters and to avoid divergence during training. This practice is called weight tying. A positional encoding
Transformer_(deep_learning)
Concept in information theory
appeared n times in the test sample of size N). By the definition of KL divergence, it is also equal to H(~p) + DKL(~p || q), which is ≥ H(~p). Consequently
Perplexity
randomization procedure. The model for the response is Y i , j = μ + T i + r a n d o m e r r o r {\displaystyle Y_{i,j}=\mu +T_{i}+\mathrm {random\
Completely_randomized_design
Process of making something random
machines, which enhance randomness beyond what manual shuffling can achieve. With the rise of online casinos, digital random number generators (RNGs)
Randomization
Form of scientific experiment
physiological effects of treatments from various psychological sources of bias.[citation needed] The randomness in the assignment of participants to
Randomized_controlled_trial
Model in theoretical ecology and statistical mechanics
The random generalized Lotka–Volterra model (rGLV) is an ecological model and random set of coupled ordinary differential equations where the parameters
Random generalized Lotka–Volterra model
Random_generalized_Lotka–Volterra_model
Concept in genetics
but also natural selection, gene flow, and mutation contribute to this divergence. This potential for relatively rapid changes in the colony's gene frequency
Genetic_drift
Model for generating observable data in probability and statistics
Generative models are a class of computational models frequently used for classification. In machine learning, it typically models the joint distribution
Generative_model
Branch of statistics
Cox proportional hazards regression Parametric survival models Survival trees Survival random forests The following terms are commonly used in survival
Survival_analysis
Statistical concept
mixture models, where members of the population are sampled at random. Conversely, mixture models can be thought of as compositional models, where the
Mixture_model
Process of using data analysis for predicting population data from sample data
(first) selecting a statistical model of the process that generates the data and (second) deducing propositions from the model. Konishi and Kitagawa state
Statistical_inference
Class of statistical models
linear model (GLM) is a flexible generalization of ordinary linear regression. The GLM generalizes linear regression by allowing the linear model to be
Generalized_linear_model
Selection of data points in statistics
estimate the accuracy of results. Simple random sampling can be vulnerable to sampling error because the randomness of the selection may result in a sample
Sampling_(statistics)
Design of tasks
may be represented with a general linear model, with the design matrix W {\displaystyle W} having entries from { − 1 , 0 , 1 } {\displaystyle \{-1,0,1\}}
Design_of_experiments
Statistical model for a binary dependent variable
Kullback–Leibler divergence. This leads to the intuition that by maximizing the log-likelihood of a model, you are minimizing the KL divergence of your model from the
Logistic_regression
Task of selecting a statistical model from a set of candidate models
Model selection is the task of selecting a model from among various candidates on the basis of performance criterion to choose the best one. In the context
Model_selection
Model of the evolution of genetic incompatibility
modes of divergence. For instance, if divergence is due to different selection pressures, thus causing natural selection to act, or to random genetic drift
Bateson–Dobzhansky–Muller model
Bateson–Dobzhansky–Muller_model
Statistical method that summarizes and/or integrates data from multiple sources
to assume that random-effects analysis accounts for all uncertainty about the way effects can vary from trial to trial. Newer models of meta-analysis
Meta-analysis
Degradation of AI models trained on synthetic data
This is the same scaling as for a single dimensional Gaussian random walk. However, divergence of the variance of X j n {\displaystyle X_{j}^{n}} does not
Model_collapse
Form of causal modeling that fit networks of constructs to data
Structural equation modeling (SEM) is a diverse set of methods used by scientists for both observational and experimental research. SEM is used mostly
Structural_equation_modeling
Metric on a smooth statistical manifold
relative entropy (i.e., the Kullback–Leibler divergence); specifically, it is the Hessian of the divergence. Alternately, it can be understood as the metric
Fisher_information_metric
Probabilistic problem-solving algorithm
random sampling for obtaining numerical results, conceptualized by Polish mathematician Stanisław Ulam. The underlying concept is to use randomness to
Monte_Carlo_method
Statistical concept
values. Graphical models can be used to describe the missing data mechanism in detail. Values in a data set are missing completely at random (MCAR) if the
Missing_data
Mathematical methods used in Bayesian inference and machine learning
Kullback–Leibler divergence (KL-divergence) of Q from P as the choice of dissimilarity function. This choice makes this minimization tractable. The KL-divergence is
Variational_Bayesian_methods
Class of statistical survival models
Proportional hazards models are a class of survival models in statistics. Survival models relate the time that passes, before some event occurs, to one
Proportional_hazards_model
Mathematical function for the probability a given outcome occurs in an experiment
probability distribution. With this source of uniform pseudo-randomness, realizations of any random variable can be generated. For example, suppose U has a
Probability_distribution
Randomization Randomized block design Randomized controlled trial Randomized decision rule Randomized experiment Randomized response Randomness Randomness tests
List_of_statistics_articles
Measure of goodness of fit for a statistical model
generalized linear models. Deviance can be related to Kullback–Leibler divergence. The unit deviance d ( y , μ ) {\displaystyle d(y,\mu )} is a bivariate
Deviance_(statistics)
Statistical model allowing for frequent zero values
conceived of as the basic count model upon which a variety of other count models are based." In a Poisson model, "… the random variable y {\displaystyle y}
Zero-inflated_model
Mathematical model used for classification or regression
Discriminative models, also referred to as conditional models, are a class of models frequently used for classification. In machine learning, it typically models the
Discriminative_model
Statistical model validation technique
rotation estimation or out-of-sample testing, is any of various similar model validation techniques for assessing how the results of a statistical analysis
Cross-validation_(statistics)
Type of statistical model
theory is possible. For the regression case, the statistical model is as follows. Given a (random) sample ( Y i , X i 1 , … , X i p ) , i = 1 , … , n {\displaystyle
Linear_model
Measure of the joint variability
producing the g factor. Another is to personality, with models like the five factor model being derived from principal component analysis. Algorithms for calculating
Covariance
Notion in statistics
of information that an observable random variable X carries about an unknown parameter θ of a distribution that models X. Formally, it is the variance of
Fisher_information
Deep learning generative model to encode data representation
optimize this model, one needs to know two terms: the "reconstruction error", and the Kullback–Leibler divergence (KL-D). Both terms are derived from the free
Variational_autoencoder
Measure of divergence between populations
measure of the genetic divergence between species or between populations within a species, whether the distance measures time from common ancestor or degree
Genetic_distance
Statistical measure of how far values spread from their average
numbers are spread out from their average value. It is defined as the expected value of the squared deviation from the mean of a random variable. The standard
Variance
Probability distribution
hold.[proof] For non-normal random variables uncorrelatedness does not imply independence. The Kullback–Leibler divergence of one normal distribution X
Normal_distribution
Statistical distribution for dependence between random variables
/ model the dependence (inter-correlation) between random variables. Their name, introduced by applied mathematician Abe Sklar in 1959, comes from the
Copula_(statistics)
Class of artificial neural network
W} , is the contrastive divergence (CD) algorithm due to Hinton, originally developed to train PoE (product of experts) models. The algorithm performs
Restricted_Boltzmann_machine
Statistical modeling method
regression, the relationships are modeled using linear predictor functions whose unknown model parameters are estimated from the data. Most commonly, the conditional
Linear_regression
Statistical method
estimator by resampling (often with replacement) one's data or a model which is estimated from the data. Bootstrapping assigns measures of accuracy (bias,
Bootstrapping_(statistics)
Sampling from a population which can be partitioned into subpopulations
interest, between the towns). Instead, if we choose to take a random sample of 10, 20 and 30 from Town A, B and C respectively, then we can produce a smaller
Stratified_sampling
Statistical model used in time series analysis
commonly normal random variables. The notation ARMA(p, q) refers to the model with p autoregressive terms and q moving-average terms. This model contains the
Autoregressive moving-average model
Autoregressive_moving-average_model
Statistical linear model
general linear model or general multivariate regression model is a compact way of simultaneously writing several multiple linear regression models. In that
General_linear_model
Experiment using randomness in some aspect, usually to aid in removal of bias
(according to the law of large numbers). Randomization also produces ignorable designs, which are valuable in model-based statistical inference, especially
Randomized_experiment
Parametric model in survival analysis
accelerated failure time model (AFT model) is a parametric model that provides an alternative to the commonly used proportional hazards models. Whereas a proportional
Accelerated failure time model
Accelerated_failure_time_model
Probability distribution
)\psi (\alpha ).} The Kullback–Leibler divergence (KL-divergence), of Gamma(αp, βp) ("true" distribution) from Gamma(αq, βq) ("approximating" distribution)
Gamma_distribution
Statistical test
time series are different from zero. Instead of testing randomness at each distinct lag, it tests the "overall" randomness based on a number of lags,
Ljung–Box_test
Probability Randomness, Pseudorandomness, Quasirandomness Randomization, hardware random number generator Random number generation Random sequence Uncertainty
List_of_probability_topics
Lower bound on the log-likelihood of some observed data
Kullback-Leibler divergence (KL divergence) term which decreases the ELBO due to an internal part of the model being inaccurate despite good fit of the model overall
Evidence_lower_bound
Set of statistical processes for estimating the relationships among variables
line case: Given a random sample from the population, we estimate the population parameters and obtain the sample linear regression model: y ^ i = β ^ 0 +
Regression_analysis
Chart of correlation statistics
processes. The randomness assumption is critically important for the following three reasons: Most standard statistical tests depend on randomness. The validity
Correlogram
Model-free reinforcement learning algorithm
Deep 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
Proximal_policy_optimization
Statistical theorem
and the restricted model is therefore not nested within the larger model. As a demonstration, they set either one or two random effects variances to
Wilks'_theorem
Statistical hypothesis test
Ryabko, B. Ya.; Stognienko, V. S.; Shokin, Yu. I. (2004). "A new test for randomness and its application to some cryptographic problems" (PDF). Journal of
Chi-squared_test
Most recent individual from which all organisms in a group are directly descended
mutations in this region. If genetic divergence and regional divergence coincide it can be concluded that the observed divergence is due to migration as evidenced
Most_recent_common_ancestor
Overview of and topical guide to statistics
Sufficient statistic Ancillary statistic Minimal sufficiency Kullback–Leibler divergence Nuisance parameter Order statistic Bayesian inference Bayes' theorem Bayes
Outline_of_statistics
Method of statistical sampling
attributes or characteristics, known as strata, then followed by simple random sampling from the stratified groups, where each element within the same subgroup
Stratified_randomization
Transport of dissolved species from the highest to the lowest concentration region
is a stochastic process due to the inherent randomness of the diffusing entity and can be used to model many real-life stochastic scenarios. Therefore
Diffusion
Statistical method
sampled from a universe of variables. All other methods assume cases to be sampled and variables fixed. Factor regression model is a combinatorial model of
Factor_analysis
Design of experiments to collect similar contexts together
trials for any K-factor randomized block design are simply the cell indices of a k dimensional matrix. The model for a randomized block design with one
Blocking_(statistics)
No spontaneous symmetry breaking in two-dimensional systems at finite temperature
where the field has the value 1, the divergence tells you that as you travel far away, the field is arbitrarily far from the starting value. This makes a
Mermin–Wagner_theorem
Process involving chance used in research for allocating experimental subjects to groups
statistics. More advanced statistical modeling can be used to adapt the inference to the sampling method. Randomization was emphasized in the theory of statistical
Random_assignment
Estimator for quality of a statistical model
information lost from using g1 to represent f by calculating the Kullback–Leibler divergence, DKL(f ‖ g1); similarly, the information lost from using g2 to
Akaike_information_criterion
Probability distribution
X_{i}]=\psi (K\alpha +1)-\psi (\alpha +1)} The Kullback–Leibler (KL) divergence between two Dirichlet distributions, Dir ( α ) {\displaystyle {\text{Dir}}({\boldsymbol
Dirichlet_distribution
Concept in statistics
"Controlling Variability in Split-Merge Systems". Analytical and Stochastic Modeling Techniques and Applications (PDF). Lecture Notes in Computer Science. Vol
Range_(statistics)
Statistical phenomenon
aap.2009.04.020. PMID 19540977. For an illustration see Nate Silver, "Randomness: Catch the Fever!", Baseball Prospectus, May 14, 2003. Flyvbjerg, Bent
Regression_toward_the_mean
Discrete probability distribution
The directed Kullback–Leibler divergence of P = Pois ( λ ) {\displaystyle P=\operatorname {Pois} (\lambda )} from P 0 = Pois ( λ 0 ) {\displaystyle
Poisson_distribution
Range to estimate an unknown parameter
data from a random sample. Because the sample is random, the interval endpoints are random variables. Let X {\displaystyle X} be a random sample from a probability
Confidence_interval
Function related to statistics and probability theory
model explains observed data by calculating the probability of seeing that data under different parameter values of the model. It is constructed from
Likelihood_function
Model in statistical genetics
coalescent model also provides a framework for using genomic data to address a number of biological problems, such as estimation of species divergence times
Multispecies coalescent process
Multispecies_coalescent_process
Study of collection and analysis of data
is conventional to begin with a statistical population or a statistical model to be studied. Populations can be diverse groups of people or objects such
Statistics
Divergent sum of positive unit fractions
_{i=1}^{n-1}2H_{i}=O(n\log n).} The divergence of the harmonic series corresponds in this application to the fact that, in the comparison model of sorting used for quicksort
Harmonic_series_(mathematics)
Kth smallest value in a statistical sample
holds in a stronger sense, such as convergence in relative entropy or KL divergence. An interesting observation can be made in the case where the distribution
Order_statistic
Statistical model used in machine learning
training a deep learning model, the goal with normalizing flows is to minimize the Kullback–Leibler divergence between the model's likelihood and the target
Flow-based_generative_model
Statistical technique correcting sampling bias
The Heckman correction is a statistical technique to correct bias from non-randomly selected samples or otherwise incidentally truncated dependent variables
Heckman_correction
with success for the Okapi BM25 model, and a multinomial language model, but also includes divergence from randomness models. The LGTE 1.1.9 and later versions
LGTE
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
Boy/Male
Scottish
Crooked.
Surname or Lastname
Jewish (from Poland)
Jewish (from Poland) : Polish spelling of the occupational surname Mintzer ‘moneyer’.English : unexplained. Perhaps a metonymic occupational name for a butcher, a cook, or a warrior, from a derivative of Middle English mince(n) ‘to mince’, ‘to cut into small pieces’.
Surname or Lastname
Americanized form of Italian Gervasio.English
Americanized form of Italian Gervasio.English : variant of Jarvis.
Surname or Lastname
Americanized form of Geman Wehry.English
Americanized form of Geman Wehry.English : nickname from Middle English wery ‘wicked’, ‘acursed’ (from Old English wearg).
Surname or Lastname
Reduced form of Irish McCarley.English
Reduced form of Irish McCarley.English : habitational name from the hamlet of Carley in Lifton, Devon, possibly named with Cornish ker ‘fort’ + Old English lēah ‘woodland clearing’.Perhaps an Americanized form of German Kehrli or Kerle (see Kerley).
Surname or Lastname
Americanized spelling of Jewish Leykin (from Belarus), a metronymic from Leyke, a pet form of the Yiddish female personal name Leye, from the Hebrew female personal name Lea, from which English Leah is derived (see Genesis 29
Americanized spelling of Jewish Leykin (from Belarus), a metronymic from Leyke, a pet form of the Yiddish female personal name Leye, from the Hebrew female personal name Lea, from which English Leah is derived (see Genesis 29 : 16) + the Slavic possessive suffix -in.English : from a medieval personal name, a diminutive of Lawrence. Compare Law 1 and Larkin.
Surname or Lastname
Americanized form of German Gehr.English
Americanized form of German Gehr.English : perhaps a variant of Geary 3.Hungarian : from a reduced form of the personal name Gergely, Latin Gregorius (see Gregory).
Surname or Lastname
Dutch
Dutch : variant of Krom.English : possibly a variant of Croom.
Surname or Lastname
English, from Welsh
English, from Welsh : from the Welsh personal name Caradog meaning ‘amiable’. A British bearer of this name is recorded in the Latin form Cara(c)tacus and remembered for his leadership of a revolt against the Roman occupation in the 1st century ad.
Surname or Lastname
Americanized spelling of German Blümle, from a pet form of Blum.English
Americanized spelling of German Blümle, from a pet form of Blum.English : variant spelling of Plumley.
Surname or Lastname
Possibly an Americanized spelling of French Cobet, from a reduced pet form of the personal name Jacob.English
Possibly an Americanized spelling of French Cobet, from a reduced pet form of the personal name Jacob.English : unexplained. Compare Coby.
Surname or Lastname
Spanish form of Basque Aldai, a habitational name from any of several places in the Basque country called Alday or Aldai, from Basque alde ‘side’, ‘slope’.Americanized form of German Aldag.English
Spanish form of Basque Aldai, a habitational name from any of several places in the Basque country called Alday or Aldai, from Basque alde ‘side’, ‘slope’.Americanized form of German Aldag.English : variant spelling of Allday.
Surname or Lastname
Americanized form of Swedish Larsson, Danish and Norwegian Larsen.English
Americanized form of Swedish Larsson, Danish and Norwegian Larsen.English : patronymic from a pet form of Lawrence.
Boy/Male
Hindu
Most Love
Surname or Lastname
North German form of Backhaus.English
North German form of Backhaus.English : variant of Backus.
Surname or Lastname
English
English : variant spelling of Frome.German : from a short form of a personal name composed with Middle High German vrom, vrum ‘valiant’, ‘steadfast’ (see Frommelt).
Surname or Lastname
Respelling of German Austel, from a pet form of August.English
Respelling of German Austel, from a pet form of August.English : possibly a variant of Astle. There is a place in Cornwall called St. Austell (from the dedication of its church to a certain St. Austol), but this is unlikely to be the source of the surname.
Surname or Lastname
North German form of Knoche.German
North German form of Knoche.German : possibly a habitational name from Knock near Emden.English : topographic name for someone living by a hill, from Middle English knocke ‘hill’ (Old English cnoc).
Surname or Lastname
Reduced form of Irish McCann.English
Reduced form of Irish McCann.English : habitational name from Cann, a place in Dorset, named from Old English canna ‘can’, used in the transferred sense of a deep valley, or a topographic name from the same word used elsewhere in southwestern England.Americanized spelling of Kann or Kahn.
Surname or Lastname
English
English : habitational name from any of various places so called from the rivers on which they stand, or simply a name for someone living beside a river of this name, which is probably cognate with Welsh ffraw ‘fair’, ‘fine’, ‘brisk’. Compare Frampton.
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
Boy/Male
Norse English Irish Shakespearean
From Denmark.
Girl/Female
English, Hindu, Indian, Marathi
Small Daughter
Girl/Female
German
Glorious
Male
Italian
Italian form of Latin Paulus, PAOLO means "small."
Male
Dutch
, mind bright.
Girl/Female
Greek
Ruler.
Boy/Male
French
Manly.
Biblical
grace,whom Jehovah gave, a name of which John is the contraction.
Boy/Male
Arabic, Muslim
Cunning
Female
English
Variant spelling of English Kaitlin, KAITLYNN means "pure."
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
DIVERGENCE FROM-RANDOMNESS-MODEL
a.
Incapable of being ransomed; without ransom.
n.
To provide with a form, as a hare. See Form, n., 9.
n.
See Divergence.
prep.
Out of the neighborhood of; lessening or losing proximity to; leaving behind; by reason of; out of; by aid of; -- used whenever departure, setting out, commencement of action, being, state, occurrence, etc., or procedure, emanation, absence, separation, etc., are to be expressed. It is construed with, and indicates, the point of space or time at which the action, state, etc., are regarded as setting out or beginning; also, less frequently, the source, the cause, the occasion, out of which anything proceeds; -- the aritithesis and correlative of to; as, it, is one hundred miles from Boston to Springfield; he took his sword from his side; light proceeds from the sun; separate the coarse wool from the fine; men have all sprung from Adam, and often go from good to bad, and from bad to worse; the merit of an action depends on the principle from which it proceeds; men judge of facts from personal knowledge, or from testimony.
n.
Alt. of Devergency
adv.
From; away; back or backward; -- now used only in opposition to the word to, in the phrase to and fro, that is, to and from. See To and fro under To.
n.
A supporting plate having raised ribs that form continuations of the rails, to guide the wheels where one track branches from another or crosses it.
a.
Fig.: Disagreeing from something given; differing; as, a divergent statement.
n.
Alt. of Divergency
n.
A receding from each other in moving from a common center; the state of being divergent; as, an angle is made by the divergence of straight lines.
adv.
In a radiate manner; with radiation or divergence from a center.
n.
Divergence.
prep.
From.
n.
Disagreement; difference.
n.
A forked exlpansion or divergence; a bifurcation; a branching.
n.
A separation into two parts or branches; a forking; a divergence.
a.
Tending in different directions from a common center; spreading apart; divergent.
a.
Causing divergence of rays; as, a divergent lens.
v. i.
To take a form, definite shape, or arrangement; as, the infantry should form in column.
a.
Receding farther and farther from each other, as lines radiating from one point; deviating gradually from a given direction; -- opposed to convergent.