Recent developments in statistical regression methodology shift away from pure mean toward distributional models. One important strand thereof is that of conditional transformation models (CTMs). CTMs infer the entire distribution directly by applying a function to response conditionally on set covariates simple log-concave reference distribution. Thereby, allow not only variance, kurtosis or s...

Learning the cumulative distribution function (CDF) of an outcome variable conditional on a set features remains challenging, especially in high-dimensional settings. Conditional transformation models provide semi-parametric approach that allows to model large class CDFs without explicit parametric assumption and with only few parameters. Existing estimation approaches within this are, however,...

investors use different approaches to select optimal portfolio. so, optimal investment choices according to return can be interpreted in different models. the traditional approach to allocate portfolio selection called a mean - variance explains. another approach is markov chain. markov chain is a random process without memory. this means that the conditional probability distribution of the nex...

Input-output hidden Markov models (IOHMM) are conditional hidden Markov models in which the emission (and possibly the transition) probabilities can be conditioned on an input sequence. For example, these conditional distributions can be linear, logistic, or nonlinear (using for example multilayer neural networks). We compare the generalization performance of several models which are special ca...

While DNN-HMM acoustic models have replaced GMMHMMs in the standard ASR pipeline due to performance improvements, one unrealistic assumption that remains in these models is the conditional independence assumption of the Hidden Markov Model (HMM). In this work, we explore the extent to which depth of neural networks helps compensate for these poor conditional independence assumptions. Using a bo...