Malba na dřevě. Postava ženy v antikizujícím oděvu, na hlavě koruna. Stojí na perspektivně zobrazené dlaždicové podlaze, v levé ruce drží meč, v pravici váhy. and Adamová, Šouša 2004#, obr. 12.
Při přestavbě budova z roku 1541 opatřena bosovaným portálem a šíty, všechny ukončeny čučky, z nichž střední sedí na konzole podobně jako na radnici v Plzni. Na křídlech postranních štítů voluty, křídla středního štítu tvoří vykrojení delfíni. Sgrafitová výzdoba mezi 2. a 3. oknem v 1. patře: Spravedlnost (váhy, meč, fascis, šátek na očích), v pásu pod štítem, ležící postavy zleva: Vulkán (kovadlina, kladivo), Neptun (trojzubec), Ceres (klasy, věnec z klasů). Ve štítě tři figury: and Šamánková 1961#, 98.
Let $X$ be a completely regular Hausdorff space, $C_{b}(X)$ the space of all scalar-valued bounded continuous functions on $X$ with strict topologies. We prove that these are locally convex topological algebras with jointly continuous multiplication. Also we find the necessary and sufficient conditions for these algebras to be locally $m$-convex.
Let $\lbrace \beta (n)\rbrace ^{\infty }_{n=0}$ be a sequence of positive numbers and $1 \le p < \infty $. We consider the space $H^{p}(\beta )$ of all power series $f(z)=\sum ^{\infty }_{n=0}\hat{f}(n)z^{n}$ such that $\sum ^{\infty }_{n=0}|\hat{f}(n)|^{p}\beta (n)^{p} < \infty $. We investigate strict cyclicity of $H^{\infty }_{p}(\beta )$, the weakly closed algebra generated by the operator of multiplication by $z$ acting on $H^{p}(\beta )$, and determine the maximal ideal space, the dual space and the reflexivity of the algebra $H^{\infty }_{p}(\beta )$. We also give a necessary condition for a composition operator to be bounded on $H^{p}(\beta )$ when $H^{\infty }_{p}(\beta )$ is strictly cyclic.
Considering the statistical recognition of multidimensional binary observations we approximato the unknown class-conditioiial probability distributions by multivariate Bernoulli mixtures. We show that both the parameter optimization and the resulting Bayesian decision-making can be realized by a probabilistic neural network having strictly modular properties. In particular, the process of learning based on the EM algorithm can be perfomied by means of a sequential autonomous adaptation of neurons involving only the infomiation from the input synapses and the interior of neurons. In this sense the probabilistic neural network can be designed automatically. The properties of the sequential strictly modular learning procedure are illustrated by mumerical exainples.