LEADER 02921oam 2200661I 450 001 9910790651303321 005 20230207232004.0 010 $a1-134-56590-9 010 $a0-415-61230-6 010 $a1-315-88916-1 010 $a1-134-56583-6 024 7 $a10.4324/9781315889160 035 $a(CKB)2550000001131342 035 $a(EBL)1474771 035 $a(OCoLC)870591470 035 $a(SSID)ssj0001157494 035 $a(PQKBManifestationID)11654538 035 $a(PQKBTitleCode)TC0001157494 035 $a(PQKBWorkID)11208663 035 $a(PQKB)11233131 035 $a(MiAaPQ)EBC1474771 035 $a(Au-PeEL)EBL1474771 035 $a(CaPaEBR)ebr10786289 035 $a(CaONFJC)MIL531051 035 $a(OCoLC)958102546 035 $a(OCoLC)868972438 035 $a(FINmELB)ELB131746 035 $a(EXLCZ)992550000001131342 100 $a20180706e20051928 uy 0 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aEngland in Shakespeare's day /$fG.B. Harrison 210 1$aLondon ;$aNew York :$cRoutledge,$d2005. 215 $a1 online resource (440 p.) 225 1 $aRoutledge library editions. Shakespeare. History & politics ;$vII 300 $aFirst published in 1928. 311 $a0-415-35311-4 311 $a1-299-99800-3 327 $asection I. The court -- section II. The service of the state -- section III. Education and youth -- section Ivolume Travel and trade -- section volume London life -- section VI. Country life -- section VII. Matters of religion -- section VIII. Men of letters -- section IX. Moods and manners. 330 $aFirst published in 1928.

This book collects together over one hundred sources by Elizabethan authors which show English life in English literature. Most of them have been selected as much to catch the atmosphere as the moods of the period, and come from the great Elizabethan writers who can transmit the essence of the time. A 'gallery of Elizabethan pictures' rather than a complete survey of life in Shakespeare's day, the spelling and punctuation have been modernized throughout. To enable those who wish to read the extracts in their context, references are given to the most accessible 410 0$aRoutledge library editions.$pShakespeare. 606 $aEnglish literature$yEarly modern, 1500-1700$xHistory and criticism 607 $aEngland$xSocial life and customs$y16th century$vSources 607 $aEngland$xIntellectual life$y16th century 607 $aEngland$vLiterary collections 615 0$aEnglish literature$xHistory and criticism. 676 $a256 700 $aHarrison$b G. B$g(George Bagshawe),$f1894-1991.,$0163927 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910790651303321 996 $aEngland in Shakespeare's day$93792203 997 $aUNINA LEADER 03586nam 22003493a 450 001 9910765778103321 005 20250203235425.0 010 $a9783038976332 010 $a3038976334 024 8 $a10.3390/books978-3-03897-633-2 035 $a(CKB)5400000000000013 035 $a(ScCtBLL)f4c82345-51f3-417f-b52a-3383ad573fbd 035 $a(OCoLC)1105786105 035 $a(EXLCZ)995400000000000013 100 $a20250203i20192019 uu 101 0 $aeng 135 $auru|||||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 00$aInformation Geometry$fGeert Verdoolaege 210 1$aBasel, Switzerland :$cMDPI,$d2019. 215 $a1 online resource (1 p.) 330 $aThis Special Issue of the journal Entropy, titled "Information Geometry I", contains a collection of 17 papers concerning the foundations and applications of information geometry. Based on a geometrical interpretation of probability, information geometry has become a rich mathematical field employing the methods of differential geometry. It has numerous applications to data science, physics, and neuroscience. Presenting original research, yet written in an accessible, tutorial style, this collection of papers will be useful for scientists who are new to the field, while providing an excellent reference for the more experienced researcher. Several papers are written by authorities in the field, and topics cover the foundations of information geometry, as well as applications to statistics, Bayesian inference, machine learning, complex systems, physics, and neuroscience. 610 00$aG)-connections; invariance; probability theory; Riemannian geometry; complexity; Bayesian prediction; Fisher information; Kullback-Leibler divergence; minimax; predictive metric; subminimax estimator; information geometry; Markov chain Monte Carlo; Bayesian inference; computational statistics; machine learning; statistical mechanics; diffusions; Fisher information metric; information geometry; convex support polytope; conditional model; Markov morphism; isometric embedding; natural gradient; bag-of-X; ?-divergence; Jeffreys divergence; centroid; k-means clustering; k-means seeding; information geometry; Boltzmann machine; Fisher information; parametric reduction; information geometry; variational Bayes; regime-switching log-normal model; model selection; covariance estimation; quantum entropy; metric; q-bit; information; geometry; geodesics; relevant entropy; Fisher information; Riemannian metric; prior distribution; univariate normal distribution; image classification; simplex; cone; exponential family; monotone likelihood ratio; unimodal; duality; information geometry; complexity measure; complex network; system decompositionability; geometric mean; statistical manifold; Riemannian Hessian; combinatorial optimization; Newton method$aG)-metric; (F$a?) -structure; information geometry; computational geometry; statistical foundations; embedding; Amari's ?-connections; F-metric; F-connections; (F$aMarkov random fields; information theory; Fisher information; entropy; maximum pseudo-likelihood estimation; Bezout matrix; Sylvester matrix; tensor Sylvester matrix; Stein equation; Vandermonde matrix; stationary process; matrix resultant; Fisher information matrix; information geometry; dually flat structure; decomposable divergence; (? 700 $aVerdoolaege$b Geert$01309762 801 0$bScCtBLL 801 1$bScCtBLL 906 $aBOOK 912 $a9910765778103321 996 $aInformation Geometry$93029571 997 $aUNINA