1 Introduction 9 Monte Carlo simulations are a class of computational algorithms that rely on repeated random sampling to compute their results. The Monte Carlo method was coined in the 1940s by John von Neumann, Stanislaw Ulam and Nicholas Metropolis, while they were working on nuclear weapon projects (Manhattan Project) in the Los Alamos National Laboratory. It was named in homage to the Monte Carlo Casino, a famous casino where Ulam's uncle would often gamble away his money [1÷ ]. Monte Carlo simulations are often used in computer simulations of physical and mathematical systems. These methods are most suited to calculation by a computer and tend to be used when it is infeasible to compute an exact result with a deterministic algorithm. Monte Carlo simulations are especially useful for simulating systems with many coupled degrees of freedom, such as fluids, disordered materials, strongly coupled solids, and cellular structures. In recent time, Monte Carlo simulations (MCS) have been increasingly used in the evaluation of measurement uncertainty so it is issued addition of a GUM: GUM 101:2008 Propagation of distributions using a Monte Carlo simulation [2]. Compared to the standardized procedures (GUM method) of calculating the measurement uncertainty, this method has a whole range of advantages, but it also has some disadvantages. However, according to the experience acquired at the Laboratory for Precise Measurement of Length (LFSB) the advantages of this method are greater, and especially at levels where it is necessary to calculate the measurement uncertainty and the knowledge (statistics, differential calculus) and experience are lacking. In other 333 S. Medić et. al. ISSN 1330-3651 UDC/UDK 53.088.3:519.245 VALIDATION OF THE REALISED MEASUREMENT UNCERTAINTY IN PROCESS OF PRECISE LINE SCALES CALIBRATION Srđan Medić, Živko Kondić, Biserka Runje The Laboratory for Precise Measurement of Length, which is at the same time the Croatian National Laboratory for Length (in text Laboratory) takes part in CIPM MRA (Comité International des Poids et Mesures, Mutual Recognition Arrangement) comparisons of length standards, which include line scales as very important standards of length.When the results reported in the comparisons, it is necessary to state the estimated measurement uncertainty. Recently, the Monte Carlo simulations (MCS) have been increasingly applied in the field of estimation measurement uncertainties. The paper presents validation of the realised measurement uncertainty by GUM method in process of precise line scales calibration using the MCS method. The MCS method is based on random number generation from the probability density functions for each input value and forming of experimental probability density function of the output value. Also, the paper presents obtained results of the international comparison measurement which representing a real validation of the device and evaluated measurement uncertainty. Keywords: calibration, line scales, measurement uncertainty, Monte Carlo simulation Original scientific paper Laboratorij za precizna mjerenja dužina koji je ujedno i Nacionalni laboratorij za duljinu sudjeluje (Laboratorij) aja i precizne mjerne skale. U usporedbenim mjerenjima pri iskazivanju rezultata mjerenja, neophodno je dati i procjenu mjerne nesigurnosti. U novije vrijeme, Monte Carlo simulacije (MCS) imaju u procjeni mjernih nesigurnosti. U radu se prezentira validacija realizirane mjerne nesigurnosti GUM metodom upotrebom MCS metode. . i . CIPM MRA (Comité International des Poids et Mesures, Mutual Recognition Arrangement) ključnim usporedbama etalona duljine među kojima su od posebnog znač sve veću primjenu MCS metoda temelji se na generiranju slučajnih brojeva iz funkcija gustoće vjerojatnosti za svaku ulaznu veličinu i stvaranju eksperimentalne funkcije gustoće vjerojatnosti izlazne veličine kombinirajući različite razdiobe kojima su definirane ulazne veličine Isto tako, u radu se prezentiraju rezultati međunarodnog usporedbenog mjerenja koji predstavljaju stvarnu validaciju mjernog uređaja i proc jenjene mjerne nesigurnosti Klučne riječi: mjerna nesigurnost, mjerne skale, Monte Carlo simulacija, umjeravanje Izvorni znanstveni članak Validacija realizirane mjerne nesigurnosti u postupku umjeravanja preciznih mjernih skala Validacija realizirane mjerne nesigurnosti u postupku umjeravanja preciznih mjernih skala Tehni ki vjesnik č 19, 2(2012) 333-339 , obtained result is experienced visually and the uncertainty calculus often turns into "fun". It is precisely the impossibility of visual presentation of the measurement uncertainty which is probably the worst drawback of the GUM method. Further, an example of comparison application of the MSC method with GUM method is presented, in the calibration procedure of precise line scale length of 100 mm, participating in the EURAMET Key Comparison, EURAMET.L-K7 ''Calibration of line scales'' (a project shared by the leading calibration institutes in the world). MCS method is based on generating random numbers from the probability density function for each input variable and the creation of experimental probability density function of output values by combined different distributions which are defined input variables. The procedure is repeated times, and on this way is created experimental probability density function of output values which is based on values. From experimental probability density function are estimated output values , the estimated standard deviation, and interval estimation 2 Estimation of measurement uncertainty by MCS method x Y M M × Y y i . . . , ) , 2 1 ( ) , 2 1 ( ÷ ÷ ø ö ç ç è æ + - M P M P Y Y 9 MCS can be stated as a step-by-step procedure [ ]: 1. Select the number of Monte Carlo trials to be made; 2. Generate vectors, by sampling from the assigned PDFs, as realizations of the (set of ) input quantities ; M M N x i