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The three-dimensional bimodal random-field Ising model is studied via a newfinite temperature numerical approach. The methods of Wang-Landau sampling andbroad histogram are implemented in a unified algorithm by using the N-foldversion of the Wang-Landau algorithm. The simulations are performed in dominantenergy subspaces, determined by the recently developed critical minimum energysubspace technique. The random fields are obtained from a bimodal distribution,that is we consider the discrete $(\pm\Delta)$ case and the model is studied oncubic lattices with sizes $4\leq L \leq 20$. In order to extract informationfor the relevant probability distributions of the specific heat andsusceptibility peaks, large samples of random field realizations are generated.The general aspects of the model's scaling behavior are discussed and theprocess of averaging finite-size anomalies in random systems is re-examinedunder the prism of the lack of self-averaging of the specific heat andsusceptibility of the model.Comment: 10 pages, 4 figures, presented at the third NEXT Sigma Phi  International Conference, Kolymbari, Greece (2005

A new comprehensive study of the 3D random-field Ising model via sampling the density of states in dominant energy subspaces