Probabilistic inference procedures are usually coded painstakingly from scratch, for each target model and each inference algorithm. We reduce this effort by generating inference procedures from models automatically. We make this code generation modular by decomposing inference algorithms into reusable program-toprogram transformations. These transformations perform exact inference as well as g...

While probabilistic graphical models are a central tool for reasoning under uncertainty in AI, they general not as expressive deep neural models, and inference is notoriously hard slow. In contrast, such sum-product networks (SPNs) capture joint distributions ensure tractable inference, but still lack the power of intractable based on networks. this paper, we introduce conditional SPNs (CSPNs)—...

Triple Modular Redundancy is widely used in dependable systems design to ensure high reliability against soft errors. Conventional TMR is effective in protecting sequential circuits but can’t mask soft errors in combinational circuits. A new redundancy technique called the Space-Time Triple Modular Redundancy is presented in this paper, which improves the soft error tolerance of the combination...

Abstract Economic growth is associated with the diversification of economic activities, which can be observed via evolution product export baskets. Exporting a new dependent on having, and acquiring, specific set capabilities, making process path-dependent. Taking an agnostic view identity here we derive probabilistic model for directed dynamical capability accumulation countries. Using interna...

In this study, we introduced a probabilistic voter, regarding symbol probabilities in decision process besides majority consensus. Conventional majority voter is independent of functionality of redundant modules. In our study, proposed probabilistic voter is designed corresponding to functionality of the redundant module. We tested probabilistic voter for 3 and 5 redundant modules with random t...

This paper deals with the modular analysis of distributed concurrent systems modelled by Petri nets. The main analysis techniques of such systems suffer from the well-known problem of the combinatory explosion of state space. In order to cope with this problem, we use a modular representation of the state space instead of the ordinary one. The modular representation, namely modular state space,...

We have recently introduced an any-space algorithm for exact inference in Bayesian networks, called Recursive Conditioning, RC, which allows one to trade space with time at increments of X-bytes, where X is the number of bytes needed to cache a floating point number. In this paper, we present three key extensions of RC. First, we modify the algorithm so it applies to more general factorizations...

We present a format for the specification of probabilistic transition systems that guarantees that bisimulation equivalence is a congruence for any operator defined in this format. In this sense, the format is somehow comparable to the ntyft/ntyxt format in a non-probabilistic setting. We also study the modular construction of probabilistic transition systems specifications and prove that some ...