Markov random fields (MRFs) are important tools in network psychometrics. While MRFs exist for continuous, binary and ordinal data, few address interval variables (IVs). This gap poses a problem, since IVs become increasingly common in psychological research. To adress it, this thesis derives two MRFs for IVs using the approach by \textcite{marsman2019characterizing}, which connects item response theory (IRT) models to MRFs. We first present two suitable IRT models - the continuous Rasch model and the extended Müller model - and then derive their corresponding MRFs. We show that the continuous Rasch model is equivalent to the rank-$d$ constrained continuous Ising model \parencite[]{Finneman2025}. We also introduce a new rank-$d$ constrained continuous Blume-Capel model and show its equivalence to the extended Müller model. Next, we show how these models compare to other existing MRFs. Finally, we study the model dynamics using mean field approximations and Gibbs sampling.