LEADER 00931nam a2200265 i 4500 001 991002043409707536 005 20020508185513.0 008 000220s1971 it ||| | ita 035 $ab10950734-39ule_inst 035 $aPARLA153485$9ExL 040 $aDip.to Filosofia$bita 082 0 $a301.42 100 1 $aEllis, Havelock$0167762 245 12$aL'arte dell'amore /$cHavelock Ellis ; introduzione di Marcello Pignatelli 260 $aRoma :$bNewton Compton italiana,$c1971 300 $a295 p. ;$c20 cm. 490 0 $aPaperbacks saggi ;$v46 500 $aDa: Studies in the psychology of sex 650 4$aMatrimonio 907 $a.b10950734$b23-02-17$c28-06-02 912 $a991002043409707536 945 $aLE005IF XXX B 48$g1$i2005000109553$lle005$o-$pE0.00$q-$rl$s- $t0$u0$v0$w0$x0$y.i11058213$z28-06-02 996 $aArte dell'amore$9863121 997 $aUNISALENTO 998 $ale005$b01-01-00$cm$da $e-$fita$git $h2$i1 LEADER 03241nam 2200457 450 001 9910819512503321 005 20230808204634.0 010 $a3-8325-8795-0 035 $a(CKB)4100000010135383 035 $a(MiAaPQ)EBC6032830 035 $a5e469731-a680-48e9-a7f7-4e00b0dd2d03 035 $a(EXLCZ)994100000010135383 100 $a20200316d2016 uy 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStochastic methods for parameter estimation and design of experiments in systems biology /$fvorgelegt von Andrei Kramer 210 1$aBerlin, Germany :$cLogos Verlag,$d[2016] 210 4$dİ2016 215 $a1 online resource (xii,137 pages) $cillustrations 300 $a"Von der Fakulta?t Konstruktions-, Produktions- und Fahrzeugtechnik der Universita?t Stuttgart zur Erlangung der Wu?rde eines Doktor- Ingenieurs (Dr.-Ing.) genehmigte Abhandlung." 311 $a3-8325-4195-0 320 $aIncludes bibliographical references (pages 127-137). 330 $aLong description: Markov Chain Monte Carlo (MCMC) methods are sampling based techniques, which use random numbers to approximate deterministic but unknown values. They can be used to obtain expected values, estimate parameters or to simply inspect the properties of a non-standard, high dimensional probability distribution. Bayesian analysis of model parameters provides the mathematical foundation for parameter estimation using such probabilistic sampling. The strengths of these stochastic methods are their robustness and relative simplicity even for nonlinear problems with dozens of parameters as well as a built-in uncertainty analysis. Because Bayesian model analysis necessarily involves the notion of prior knowledge, the estimation of unidentifiable parameters can be regularised (by priors) in a straight forward way. This work draws the focus on typical cases in systems biology: relative data, nonlinear ordinary differential equation models and few data points. It also investigates the consequences of parameter estimation from steady state data; consequences such as performance benefits. In biology the data is almost exclusively relative, the raw measurements (e.g. western blot intensities) are normalised by control experiments or a reference value within a series and require the model to do the same when comparing its output to the data. Several sampling algorithms are compared in terms of effective sampling speed and necessary adaptations to relative and steady state data are explained. 606 $aStochastic analysis$xMathematical models 606 $aSystems biology$xStatistical mehods 606 $aBiological systems$xData processing 615 0$aStochastic analysis$xMathematical models. 615 0$aSystems biology$xStatistical mehods. 615 0$aBiological systems$xData processing. 676 $a570.113 700 $aKramer$b Andrei$01661165 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910819512503321 996 $aStochastic methods for parameter estimation and design of experiments in systems biology$94016924 997 $aUNINA