Posts filled under #romics

Ed oggi vi esco anche la

Ed oggi vi esco anche la prima foto con Sascha. Quel giorno eravamo uno pi stanco dell'altro ahahaha (si intravede dalle nostre facce) Dovete sapere che Sascha una delle persone che mi somiglia di pi, sar per il segno zodiacale, sar quel che sar, ma siamo davvero simili. #animah #romics #memories

@Regrann from @diabolikfo

@Regrann from @diabolikforum Se ne andato Sergio Zaniboni uno dei pi importanti fumettisti italiani, il principale disegnatore di Diabolik. Ha fatto sognare tutti gli amanti della coppia di ladri pi bella in assoluto. Buon viaggio #diabolik #fumetti #comics #illustration #Italy #zaniboni #romics

In these days I was think

In these days I was thinking about how much I love my hobby and my desire to improve more and more. I want to continue sharing my passion and my works with you guys, hoping you'll continue to appreciate it. I want to talk with you, I want to know you, I want to create a little personal world, with my hobby and you, where we can interact together. I hope to know more of you, and I hope that more and more people will know me. I want to share a message of cohesion, love and solidarity with all the cosplayers I will meet in the future, we are a big family! #juuzou #juuzousuzuya #juuzousuzuyatokyoghoul #suzuyajuuzou #tokyoghoul #tg #tokyoghoulre #juuzoucosplay #suzuyacosplay #juuzousuzuyacosplay #juuzoucosplayer #romics #romics2017 #romicsaprile #cosplay #cosplayworld #cosplayer #italiancosplayer #instacosplay #starofcosplay #starsofcosplay #starofcosplaygirl #cosplaymodel #cosplayphoto #cosplaygirl #cosplaylover #cosplaylovers #cosplaying #italiancosplay

@Regrann from @love.rush_

@Regrann from @love.rush_ - Iniziamo a postare le foto del Romics~ Mi sono divertita molto domenica e abbiamo fatto un sacco di belle fotine quindi state prontissimi u.u Purtroppo passava tantissima gente e non riuscivamo a sentire molto bene la musica, ma faremo uscire qualche spezzone dell'esibizione :3 - ~Nozomi - [Ph: @lokidphcosplay] - #lovelive #lovelivecosplay #cosplaylovelive #nozomi #nozomitojo #nozomicosplay #cosplaynozomi #cosplay #cosplayer #italiancosplay #italiancosplayer #cosplayitalia #cosplayitaliani #sif #loveliveschoolidolproject #romics #romics2017 #romics17 - #regrann

An extract on #romics

The Fourier transform of a normal density f {\displaystyle f} with mean {\displaystyle \mu } and standard deviation {\displaystyle \sigma } is f ^ ( t ) = f ( x ) e i t x d x = e i t e 1 2 ( t ) 2 {\displaystyle {\hat {f}}(t)=\int _{-\infty }^{\infty }\!f(x)e^{-itx}\,dx=e^{-i\mu t}e^{-{\frac {1}{2}}(\sigma t)^{2}}} where i {\displaystyle i} is the imaginary unit. If the mean = 0 {\displaystyle \mu =0} , the first factor is 1, and the Fourier transform is, apart from a constant factor, a normal density on the frequency domain, with mean 0 and standard deviation 1 / {\displaystyle 1/\sigma } . In particular, the standard normal distribution {\displaystyle \varphi } is an eigenfunction of the Fourier transform. In probability theory, the Fourier transform of the probability distribution of a real-valued random variable X {\displaystyle X} is called the characteristic function of that variable, and can be defined as the expected value of e i t X {\displaystyle e^{itX}} , as a function of the real variable t {\displaystyle t} (the frequency parameter of the Fourier transform). This definition can be analytically extended to a complex-value variable t {\displaystyle t} .

The family of normal distributions is closed under linear transformations: if X is normally distributed with mean and standard deviation , then the variable Y = aX + b, for any real numbers a and b, is also normally distributed, with mean a + b and standard deviation |a|. Also if X1 and X2 are two independent normal random variables, with means 1, 2 and standard deviations 1, 2, then their sum X1 + X2 will also be normally distributed,[proof] with mean 1 + 2 and variance 1 2 + 2 2 {\displaystyle \sigma _{1}^{2}+\sigma _{2}^{2}} . In particular, if X and Y are independent normal deviates with zero mean and variance 2, then X + Y and X Y are also independent and normally distributed, with zero mean and variance 22. This is a special case of the polarization identity. Also, if X1, X2 are two independent normal deviates with mean and deviation , and a, b are arbitrary real numbers, then the variable X 3 = a X 1 + b X 2 ( a + b ) a 2 + b 2 + {\displaystyle X_{3}={\frac {aX_{1}+bX_{2}-(a+b)\mu }{\sqrt {a^{2}+b^{2}}}}+\mu } is also normally distributed with mean and deviation . It follows that the normal distribution is stable (with exponent = 2). More generally, any linear combination of independent normal deviates is a normal deviate.

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