Financial performance of insurance companies is captured by changes in rating grades. An
insurer is susceptible to a rating transition which is a signal depicting current financial …

Euclidean Markov decision processes are a powerful tool for modeling control problems
under uncertainty over continuous domains. Finite state imprecise, Markov decision …

One of the central considerations in the theory of Markov chains is their convergence to an
equilibrium. Coefficients of ergodicity provide an efficient method for such an analysis …

We consider convergence of Markov chains with uncertain parameters, known as imprecise
Markov chains, which contain an absorbing state. We prove that under conditioning on non …

This paper proposes a method for fitting a two-state imprecise Markov chain to time series
data from a two-state non-Markovian process. Such non-Markovian processes are common …

The determinants that affect the financial performance of an insurance company are
complicated due to the intangible nature of insurance products and the lack of transparency …

Abstract Hidden Markov models (HMMs) are widely used probabilistic models of sequential
data. As with other probabilistic models, they require the specification of local conditional …

Hidden Markov models (HMMs) are widely used models for sequential data. As with other
probabilistic models, they require the specification of local conditional probability …

Many Markov chains with a single absorbing state have a unique limiting conditional
distribution (LCD) to which they converge, conditioned on non-absorption, regardless of the …

Hidden Markov models (HMMs) are widely used models for sequential data. As with other
probabilistic graphical models, they require the specification of precise probability values …