Image Lena stasjon.jpg thumb 250px Original image Image Lena stasjon edges.png thumb 250px Edge map inverted Image Lena stasjon Otsu.png thumb 250px Thresholded edge map using Otsu s algorithm Image Lena stasjon Rosin.png thumb 250px Thresholded edge map using Rosin s algorithm Most algorithms for Thresholding image processing automatic image threshold selection assume that the intensity histogram is multi modal typically bimodal. However, some types of images are essentially unimodal since a much larger proportion of just one class of pixels e.g. the background is present in the image, and dominates the histogram. In such circumstances many of the standard threshold selection algorithms will fail. However, a few algorithms have been designed to specifically cope with such images. Methods Some examples of unimodal image threshold selection algorithms are T point algorithm the tail of the histogram is fitted by two line segments, and the threshold is selected at their intersection ref cite journal last Coudray first Nicholas coauthors Buessler, Urban title Robust threshold estimation for images with unimodal histograms journal Pattern Recognition Letters year 2010 volume 31 issue 9 pages 1010 1019 ref maximum deviation algorithm a straight line is drawn from the histogram peak to the end of the tail, and the threshold is selected at the point of the histogram furthest from the straight line ref cite journal last Rosin first Paul L. title Unimodal thresholding journal Pattern Recognition year 2001 volume 34 issue 11 pages 2083 2096 ref Rayleigh distribution model algorithm the mode peak is assumed to correspond to noise. The user specifies an allowable proportion of noise from which the threshold is determined using the model ref cite journal last Voorhees first Harry coauthors Poggio title Detecting textons and texture boundaries in natural images journal IEEE International Conference on Computer Vision year 1987 pages 250 258 ref Citations reflist Category Image pro ... more details
a unique mode statistics mode . ref MathWorld urlname Unimodal title Unimodal ref Unimodal probability ... distributions, an example of unimodal distribution. Image Bimodal.png thumb Figure 2. pdf of a simple ... which, though strictly unimodal, is usually referred to as bimodal. In statistics , a unimodal probability distribution or when referring to the distribution, a unimodal distribution is a probability distribution which has a single mode. As the term mode has multiple meanings, so does the term unimodal ..., the distribution function is called unimodal . If it has more modes it is bimodal 2 , trimodal 3 , etc ... distribution s, which are unimodal. Other examples of unimodal distributions include Cauchy distribution .... Figure 3 illustrates a distribution which by strict definition is unimodal. However, confusingly ... A.Ya. Khinchin title On unimodal distributions journal Trams. Res. Inst. Math. Mech. publisher University ... is unimodal, m being the mode. Note that under this definition the uniform distribution continuous uniform distribution is unimodal, ref Springer title Unimodal distribution id U u095330 first N.G. ... Another way to define a unimodal discrete distribution is by the occurrence of sign changes in the sequence ... with a probability mass function , math p n n dots, 1, 0, 1, dots math , is called unimodal ... author D. F. Vysochanskij, Y. I. Petunin year 1980 title Justification of the 3&sigma rule for unimodal ... inequality refines this to even nearer values, provided that the distribution function is unimodal. Further ... first2 S.H. title Chebyshev inequalities for unimodal distributions jstor 2684690 year 1997 journal ... 10.2307 2684690 ref Unimodal function As the term modal applies to data sets and probability distribution, and not in general to functions, the definitions above do not apply. The definition of unimodal ... mathematics function f x is a unimodal function if for some value m , it is monotonic ally increasing ... local maxima. Examples of unimodal functions include Quadratic polynomial functions ... more details
Orphan date February 2009 File HOF ex1.gif right 450px So called Huisman Olff Fresco models HOF models are a hierarchical set of 5 models with increasing complexity, designated for fitting unimodal species response curves on ecological gradient . External links The models are first presented in Huisman J., Olff H. & Fresco L.F.M. 1993 . A hierarchical set of models for species response analysis. Journal of Vegetation Science, 4, 37 46 http www.rug.nl biologie onderzoek onderzoekgroepen cocon Publications pdf 1993 JVegSci4 37 42.pdf pdf http cc.oulu.fi jarioksa pages hof2.htm Oksanen s introduction of HOF models, and software to compute them Category Ecological theories ... more details
The Milnor Thurston kneading theory is a mathematics mathematical theory which analyzes the iterates of piecewise monotone map mathematics mappings of an interval into itself. The emphasis is on understanding the properties of the mapping that are invariant under topological conjugacy . The theory had been developed by John Milnor and William Thurston in two widely circulated and influential Princeton preprints from 1977 that were revised in 1981 and finally published in 1988. Applications of the theory include piecewise linear models, counting of fixed point mathematics fixed points , computing the total variation, and constructing an invariant measure with maximal entropy. Short description Kneading theory provides an effective calculus for describing the qualitative behavior of the iterated function iterates of a piecewise monotonic function monotone mapping f of a closed interval I of the real line into itself. Some quantitative invariants of this discrete dynamical system , such as the lap numbers of the iterates and the Artin Mazur zeta function of f are expressed in terms of certain matrix mathematics matrices and formal power series . The basic invariant of f is its kneading matrix , a rectangular matrix with coefficients in the ring Z nowiki nowiki t nowiki nowiki of integer formal power series. A closely related kneading determinant is a formal power series math D t 1 D 1 t D 2 t 2 cdots , math with odd integer coefficients. In the simplest case when the map is unimodal function unimodal , with a maximum at c , each coefficient D sub k sub is either 1 or &minus 1, according to whether the k 1 st iterate f sup k 1 sup has local maximum or local minimum at c . See also Sharkovsky theorem Topological entropy References John Milnor and William Thurston , On iterated maps of the interval . Dynamical systems College Park, MD, 1986 87 , Lecture Notes in Math., 1342, 465 563, Springer, Berlin, 1988 MathSciNet id 0970571 Chris Preston, What you ne ... more details
In statistics , the exponentiated Weibull family of probability distribution s was introduced by Mudholkar and Srivastava 1993 as an extension of the Weibull distribution Weibull family obtained by adding a second shape parameter . The cumulative distribution function for the exponentiated Weibull distribution is math F x k, lambda alpha left 1 e x lambda k right alpha , math for x 0, and F x k 0 for x 0. Here k 0 is the first shape parameter , 0 is the second shape parameter and 0 is the scale parameter of the distribution. There are two important special cases &alpha 1 gives the Weibull distribution k 1 gives the exponentiated exponential distribution . Background The family of distributions accommodates Unimodal function unimodal , Bathtub curve bathtub shaped ref cite web url http www.sys ev.com reliability01.htm title System evolution and reliability of systems publisher Sysev Belgium date 2010 01 01 ref and Monotonic function monotone failure failure rate rate s. A similar distribution was introduced in 1984 by Zacks, called a Weibull exponential distribution Zacks 1984 . Mudholkar, Srivastava, and Kollia 1996 applied the generalized Weibull distribution to model survival data. They showed that the distribution has increasing, decreasing, bathtub, and unimodal hazard function s. Mudholkar, Srivastava, and Freimer 1995 , Mudholkar and Hutson 1996 and Nassar and Eissa 2003 studied various properties of the exponentiated Weibull distribution. Mudholkar et al. 1995 applied the exponentiated Weibull distribution to model failure data. Mudholkar and Hutson 1996 applied the exponentiated Weibull distribution to extreme value data. They showed that the exponentiated Weibull distribution has increasing, decreasing, bathtub, and unimodal hazard rates. The exponentiated exponential distribution proposed by Gupta and Kundu 1999, 2001 is a special case of the exponentiated Weibu ... more details
In mathematics , a sequence math a sub 0 sub , a sub 1 sub , ..., a sub n sub of nonnegative real numbers is called a logarithmically concave sequence , or a log concave sequence for short, if math a sub i sub sup 2 sup a sub i 1 sub a sub i 1 sub holds for math 0 i n . Examples of log concave sequences are given by the binomial coefficient s along any row of Pascal s triangle . References Reflist cite journal last Stanley first R. P. authorlink Richard P. Stanley title Log Concave and Unimodal Sequences in Algebra, Combinatorics, and Geometry journal Annals of the New York Academy of Sciences year 1989 month December volume 576 pages 500 535 doi 10.1111 j.1749 6632.1989.tb16434.x See also Unimodality Logarithmically concave function Logarithmically concave measure Category Sequences and series combin stub ... more details
In probability theory , Gauss s inequality or the Gauss inequality gives an upper bound on the probability that a unimodal random variable lies more than any given distance from its mode statistics mode . Let X be a unimodal random variable with mode m , and let &tau sup 2 sup be the expected value of X &minus m sup 2 sup . &tau sup 2 sup can also be expressed as &mu &minus m sup 2 sup &sigma sup 2 sup , where &mu and &sigma are the mean and standard deviation of X . Then for any positive value of k , math Pr mid X m mid k leq begin cases left frac 2 tau 3k right 2 & text if k geq frac 2 tau sqrt 3 6pt 1 frac k tau sqrt 3 & text if 0 leq k leq frac 2 tau sqrt 3 . end cases math The theorem was first proved by Carl Friedrich Gauss in 1823. See also Vysochanski Petunin inequality , a similar result for the distance from the mean rather than the mode Chebyshev s inequality , concerns distance from the mean without requiring unimodality References cite journal last Gauss first C. F. authorlink Carl Friedrich Gauss date 1823 title Theoria Combinationis Observationum Erroribus Minimis Obnoxiae, Pars Prior journal Commentationes Societatis Regiae Scientiarum Gottingensis Recentiores volume 5 cite book title A Dictionary of Statistics publisher Oxford University Press last Upton first Graham coauthors Cook, Ian year 2008 chapter Gauss inequality url http www.answers.com topic gauss inequality Cite journal doi 10.2307 2684690 title Chebyshev inequalities for unimodal distributions year 1997 journal American Statistician volume 51 issue 1 pages 34 40 last1 Sellke first1 T.M. last2 Sellke first2 S.H. publisher American Statistical Association jstor 2684690 Cite journal doi 10.2307 2684253 title The Three Sigma Rule year 1994 author Pukelsheim, F. journal American Statistician volume 48 issue 2 pages 88 91 publisher American Statistical Association jstor 2684253 Category Probabilistic inequalities ro Inegalitatea lui Gauss ... more details
distribution multimodal as opposed to unimodal distribution unimodal . In Reflection symmetry symmetric unimodal distributions, such as the Normal distribution normal or Gaussian distribution ... sensitive. In continuous unimodal distribution s the median lies, as a rule of thumb, between ..., median, mode, and standard deviation in a unimodal distribution ref ref Paul T. von Hippel ... Rule . J. of Statistics Education 13 2 2005 ref For unimodal distributions, the mode is within math ... hgb mode.pdf Maximum distance between the mode and the mean of a unimodal distribution ref Example for a skewed ... Neerlandica 33 1 1 5 ref Unimodal distributions The difference between the mean and the mode in a unimodal distribution is bounded by 3 sup 1 2 sup ref name unimodal http www.btinternet.com se16 hgb ... and shows that this statistic for a unimodal distribution is bounded by 3 sup 1 2 sup . The difference between the mode and the median has the same bound. ref name unimodal http www.btinternet.com ... deviation leq 3 1 2 math See also Portal Statistics unimodal function summary statistics descriptive ... more details
Hypsometry from Greek language Greek , hupsos , height ref http www.perseus.tufts.edu hopper text?doc Perseus 3Atext 3A1999.04.0057 3Aentry 3Du 28 2Fyos , Henry George Liddell, Robert Scott, A Greek English Lexicon , on Perseus ref and , metron , measure ref http www.perseus.tufts.edu hopper text?doc Perseus 3Atext 3A1999.04.0057 3Aentry 3Dme 2Ftron , Henry George Liddell, Robert Scott, A Greek English Lexicon , on Perseus ref is the measurement of land elevation relative to sea level . ref cite web title MSN Encarta url http encarta.msn.com dictionary 1861619735 hypsometry.html work archiveurl http www.webcitation.org 5kwr8Xfwz archivedate 2009 10 31 deadurl yes ref Bathymetry is the underwater equivalent. A hypsometer is an instrument used in hypsometry, which estimates the elevation by boiling water &mdash water boils at different temperatures depending on the air pressure, and thus altitude. On Earth, the elevations can take on either positive and negative underwater values, and are bimodal due to the contrast between the continents and oceans. On other planets within this solar system, elevations are typically unimodal , due to the lack of oceans on those bodies. References reflist See also Hypsometric curve Hypsometric equation Hypsometric tint Category Physical geography Category Geomorphology Category Greek loanwords Cartography stub cs Hypsometrie de Hypsometrie es Altimetr a io Hipsometrio pl Hipsometria pt Hipsometria tr zohips harita ... more details
A ternary search algorithm is a technique in computer science for finding the maxima and minima minimum or maximum of a unimodal function function mathematics function that is either increasing function strictly increasing and then strictly decreasing or vice versa . A ternary search determines either that the minimum or maximum cannot be in the first third of the domain or that it cannot be in the last third of the domain, then repeats on the remaining two thirds. A ternary search is an example of a divide and conquer algorithm see search algorithm . The function Assume we are looking for a maximum of f x and that we know the maximum lies somewhere between A and B . For the algorithm to be applicable, there must be some value x such that for all a , b with A a < b x , we have f a < f b , and for all a , b with x a < b B, we have f a > f b . Algorithm Let a unimodal function f x on some interval l r . Take any two points m1 and m2 in this segment l m1 m2 r . Then there are three possibilities if f m1 f m2 , then the required maximum can not be located on the left side l m1 . It means that the maximum further makes sense to look only in the interval m1 r if f m1 f m2 , that the situation is similar to the previous, up to symmetry. Now, the required maximum can not be in the right side m2 r , so go to the segment l m2 if f m1 f m2 , than the search should be conducted in m1 m2 , but this case can be attributed to any of the previous two in order to simplify the code . Sooner or later the length of the segment will be a little less than a predetermined constant, and the process can be stopped. choice points m1 and m2 m1 l r l 3 m2 r r l 3 Run Time Order T n T 2 3 n c log n Recursive algorithm source lang python def ternarySearch f, left, right, absolutePrecision left and right are the current bounds the maximum is between them if right left absolutePrecision return left right 2 leftThird 2 left right 3 rightThird left 2 right 3 if f leftThird f rightThird ret ... more details
and chemical engineering . ref name lee99phase Motivation Image Random sampling unimodal ... an improvement through uniform random sampling. Image Random sampling unimodal function 2 ... maintains a box from which it samples points randomly, using a uniform distribution on the box. For a unimodal ... . The worst case complexity of minimization on the class of unimodal functions grows exponentially ... generated by uni extremal that is, unimodal but not convex functions. ref harvtxt Nemirovsky ... more details
Unreferenced date December 2009 The Entorhinal Cortex EC is a major part of the hippocampal formation of the human brain , and is reciprocally connected with the hippocampus . The hippocampal formation, which consists of the hippocampus, perirhinal cortex , the dentate gyrus , the subiculum subicular areas and EC forms one of the most important parts of limbic system . It is an infolding of the parahippocampal gyrus into the inferior temporal horn of the lateral Ventricle heart ventricle . Role in knowledge processing and memory Studies, with human patients and with experimental animals, suggest that knowledge stored as explicit memory is first acquired through processing in one or more of the three polymodal association cortices prefrontal cortex prefrontal , limbic, and parieto occipital temporal to form visual, auditory and somatic information. From there, the information is then conveyed in series to the parahippocampal and perirhinal cortices, then onwards to the EC, dentate gyrus, hippocampus, subiculum and then finally back to the EC. From the EC, the information is sent back to the parahippocampal and perirhinal cortex, and finally back to the polymodal association areas of neocortex . The EC has dual functions in processing information for explicit memory storage First, it is the main input to the hippocampus. The EC projects to the dentate gyrus via the perforant pathway and by this means provides the critical input pathway in this area of the brain, linking the association cortices to the hippocampus. Second, the EC is also the major output of the hippocampus. The information coming to the hippocampus from both the poly and unimodal association cortices, converge in the EC. Role in epilepsy The entorhinal cortex and its links to the hippocampus have been implicated in the generation of seizure s in temporal lobe epilepsy , one of the most common forms of epilepsy. This, coupled with the rich innervation of the hippocampus, is the reason why the EC is so w ... more details
In statistics , the concept of the shape of the distribution refers to the shape of a probability distribution and it most often arises in questions of finding an appropriate distribution to use to model the statistical properties of a population, given a sample from that population. The shape of a distribution may be considered either descriptively, using terms such as J shaped , or numerically, using quantitative measures such as skewness and kurtosis . Considerations of the shape of a distribution arise in statistical data analysis , where simple quantitative descriptive statistics and plotting techniques such as histograms can lead on to the selection of a particular family of distributions for modelling purposes. File Standard deviation diagram.svg right thumb 350px The Normal distribution , often called the bell curve Image Exponential distribution pdf.png thumb Exponential distribution Descriptions of shape The shape of a distribution will fall somewhere in a continuum where a flat distribution might be considered central and where types of departure from this include mounded or unimodal , U shaped, J shaped, reverse J shaped and multi modal. ref Yule & Kendall 1950 Chapter 4 &mdash Frequency Distributions ref A bimodal distribution would have two high points rather than one. The shape of a distribution is sometimes characterised by the behaviours of the tails as in a long or short tail . For example a flat distribution can be said either to have no tails, or to have short tails. A normal distribution is usually regarded as having short tails, while an exponential distribution has exponential tails and a Pareto distribution has long tails. Even in the relatively simple case of a mounded distribution, the distribution may be skewed to the left or skewed to the right, with symmetric corresponding to no skew. See also Shape parameter List of probability distributions Notes Reflist References Yule, G.U., Kendall, M.G. 1950 An Introduction to the Theory of Statist ... more details
Successive parabolic interpolation is a technique for finding the extremum minimum or maximum of a continuous unimodal function by successively fitting parabola s polynomials of degree two to the function at three unique points, and at each iteration replacing the oldest point with the extremum of the fitted parabola. Advantages Only function values are used, and when this method converges to an extremum, it does so with a rate of convergence of approximately 1.324 . The superlinear rate of convergence is superior to that of other methods with only linear convergence such as line search . Moreover, not requiring the computation or approximation of function derivative s makes successive parabolic interpolation a popular alternative to other methods that do require them such as gradient descent and Newton s method in optimization Newton s method . Disadvantages On the other hand, convergence even to a local extremum is not guaranteed when using this method in isolation. For example, if the three points are line mathematics collinear , the resulting parabola is degeneracy mathematics degenerate and thus does not provide a new candidate point. Furthermore, if function derivatives are available, Newton s method is applicable and exhibits quadratic convergence. Improvements Alternating the parabolic iterations with a more robust method golden section search is a popular choice to choose candidates can greatly increase the probability of convergence without hampering the convergence rate. See also Inverse quadratic interpolation is a related method that uses parabolas to find root of a function root s rather than extrema. Simpson s rule uses parabolas to approximate definite integrals. External links http www.cse.uiuc.edu iem optimization SuccessiveParabolic An interactive demonstration References cite book author Michael Heath computer scientist Michael Heath title Scientific Computing An Introductory Survey publisher McGraw Hill location New York year 2002 pages edition ... more details
the paper claims 407 miles gallon for SkyTran s current two passenger tandem design, though the Unimodal ... continues to speed by on its main line. Unimodal hired a NASA subcontractor to build simulations of the vehicle and dynamics using funding from a US DOT grant. Unimodal has also built a prototype vehicle ... NASA Unimodal Space Act Agreement vehicle photo ref Interest from Local Government Mountain View ... more details
Image GoldenSectionSearch.png thumb 325px right Diagram of a golden section search The golden section search is a technique for finding the extremum minimum or maximum of a unimodal function by successively narrowing the range of values inside which the extremum is known to exist. The technique derives its name from the fact that the algorithm maintains the function values for triples of points whose distances form a golden ratio . The algorithm is closely related to a Fibonacci search also described below and to a binary search . Fibonacci search and Golden section search were introduced by Jack Kiefer mathematician Kiefer 1953 . see also Avriel and Wilde 1966 . Basic idea The diagram above illustrates a single step in the technique for finding a minimum. The functional values of math f x math are on the vertical axis, and the horizontal axis is the x parameter. The value of math f x math has already been evaluated at the three points math x 1 math , math x 2 math , and math x 3 math . Since math f 2 math is smaller than either math f 1 math or math f 3 math , it is clear that a minimum lies inside the interval from math x 1 math to math x 3 math since f is Unimodal function unimodal . The next step in the minimization process is to probe the function by evaluating it at a new value of x , namely math x 4 math . It is most efficient to choose math x 4 math somewhere inside the largest interval, i.e. between math x 2 math and math x 3 math . From the diagram, it is clear that if the function yields math f 4a math then a minimum lies between math x 1 math and math x 4 math and the new triplet of points will be math x 1 math , math x 2 math , and math x 4 math . However if the function yields the value math f 4b math then a minimum lies between math x 2 math and math x 3 math , and the new triplet of points will be math x 2 math , math x 4 math , and math x 3 math . Thus, in either case, we can construct a new narrower search interval that is guaranteed to contain the ... more details
of a sample from a normal distribution it looks fairly symmetric and unimodal gallery This is a sample ... distribution it looks unimodal and skewed right. gallery This is a sample of size 50 from a uniform ... more details
s10958 006 0366 5 . ref For the extension of the Theorem to all symmetric unimodal distribution s one can start with a classical result of Aleksandr Khinchin namely that all symmetric unimodal distributions ... more details
dablink This article is about a pseudonymous attribution. For people named G. W. Peck, see G. W. Peck disambiguation . G. W. Peck is a pseudonym ous attribution used as the author or co author of a number of published mathematics academic paper s. Peck is sometimes humorously identified with George Wilbur Peck , a former governor of the United States US state of Wisconsin . ref name kcc citation last Peck first G. W. doi 10.1016 S0012 365X 02 00595 2 mr 1935723 issue 2 3 journal Discrete Mathematics journal Discrete Mathematics pages 193 224 title Kleitman and combinatorics a celebration volume 257 year 2002 . ref Peck first appeared as the official author of a 1979 paper entitled Maximum antichain s of rectangular arrays . ref citation last Peck first G. W. doi 10.1016 0097 3165 79 90035 9 mr 0555816 issue 3 journal Journal of Combinatorial Theory Journal of Combinatorial Theory, Series A pages 397 400 title Maximum antichains of rectangular arrays volume 27 year 1979 . ref The name G. W. Peck is derived from the initials of the actual writers of this paper Ronald Graham , Douglas West mathematician Douglas West , George B. Purdy , Paul Erd s , Fan Chung , and Daniel Kleitman . Since then, Peck s name has appeared on some sixteen publications, ref http www.ams.org mathscinet search publications.html?pg1 IID&s1 202591 Listing of Peck s publications in MathSciNet subscription required , retrieved 2010 03 11. ref primarily as a pseudonym of Daniel Kleitman. ref name kcc In reference to G. W. Peck , Richard P. Stanley defined a Peck poset to be a graded poset graded partially ordered set that is rank symmetric , rank unimodal , and strongly Sperner . ref citation last Stanley first Richard doi 10.1007 BF00396271 mr 0745587 issue 1 journal Order pages 29 34 title Quotients of Peck posets volume 1 year 1984 . ref The poset s in the original paper by G. W. Peck are not Peck posets, as they lack the rank symmetric property. See also Nicolas Bourbaki Arthur Besse References ... more details
se16 hgb median.htm Relationship between the mean, median, mode, and standard deviation in a unimodal ... v13n2 vonhippel.html Mean, Median, and Skew Correcting a Textbook Rule . ref It is however known that for unimodal ... more details
Bayesian statistics In Bayesian statistics , a credible interval or Bayesian confidence interval is an interval in the domain of a Posterior distribution posterior probability distribution used for interval estimation ref Edwards, W., Lindman, H., Savage, L.J. 1963 Bayesian statistical inference in statistical research . Psychological Research , 70 , 193 242 ref . The generalisation to multivariate problems is the credible region . Credible intervals are analogous to confidence interval s in frequentist statistics ref Lee, P.M. 1997 Bayesian Statistics An Introduction , Arnold. ISBN 0 340 67785 6 ref . For example, in an experiment that determines the uncertainty distribution of parameter math t math , if the probability that math t math lies between 35 and 45 is 90 , then math 35 le t le 45 math is a 90 credible interval. Choosing a credible interval Credible intervals are not unique on a posterior distribution. Methods for defining a suitable credible interval include Choosing the narrowest interval, which for a unimodal distribution will involve choosing those values of highest probability density including the mode statistics mode . Choosing the interval where the probability of being below the interval is as likely as being above it. This interval will include the median statistics median . Assuming the mean exists, choosing the interval for which the mean statistics mean is the central point. It is possible to frame the choice of a credible interval within decision theory and, in that context, an optimal interval will always be a highest probability density set. ref O Hagan, A. 1994 Kendall s Advance Theory of Statistics, Vol 2B, Bayesian Inference , Section 2.51. Arnold, ISBN 0 340 52922 9 ref Contrasts with confidence interval A frequentist 90 confidence interval of 35&ndash 45 means that with a large number of repeated samples, 90 of the calculated confidence intervals would include the true value of the parameter. The probability that the parameter is insi ... more details
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