4 research outputs found
On the expressibility of stochastic switching circuits
Stochastic switching circuits are relay circuits that consist of stochastic switches (that we call pswitches). We study the expressive power of these circuits; in particular, we address the following basic question: given an arbitrary integer q, and a pswitch set {1/q, 2/q, ..., q-1/q}, can we realize any rational probability with denominator q^n (for arbitrary n) by a simple series-parallel stochastic switching circuit? In this paper, we generalized previous results and prove that when q is a multiple of 2 or 3 the answer is positive. We also show that when q is a prime number the answer is negative. In addition, we prove that any desired probability can be approximated well by a linear in n size circuit, with error less than q^(-n)
Synthesis of Stochastic Flow Networks
A stochastic flow network is a directed graph with incoming edges (inputs)
and outgoing edges (outputs), tokens enter through the input edges, travel
stochastically in the network, and can exit the network through the output
edges. Each node in the network is a splitter, namely, a token can enter a node
through an incoming edge and exit on one of the output edges according to a
predefined probability distribution. Stochastic flow networks can be easily
implemented by DNA-based chemical reactions, with promising applications in
molecular computing and stochastic computing. In this paper, we address a
fundamental synthesis question: Given a finite set of possible splitters and an
arbitrary rational probability distribution, design a stochastic flow network,
such that every token that enters the input edge will exit the outputs with the
prescribed probability distribution.
The problem of probability transformation dates back to von Neumann's 1951
work and was followed, among others, by Knuth and Yao in 1976. Most existing
works have been focusing on the "simulation" of target distributions. In this
paper, we design optimal-sized stochastic flow networks for "synthesizing"
target distributions. It shows that when each splitter has two outgoing edges
and is unbiased, an arbitrary rational probability \frac{a}{b} with a\leq b\leq
2^n can be realized by a stochastic flow network of size n that is optimal.
Compared to the other stochastic systems, feedback (cycles in networks)
strongly improves the expressibility of stochastic flow networks.Comment: 2 columns, 15 page
Generating Probability Distributions using Multivalued Stochastic Relay Circuits
The problem of random number generation dates back to von Neumann's work in
1951. Since then, many algorithms have been developed for generating unbiased
bits from complex correlated sources as well as for generating arbitrary
distributions from unbiased bits. An equally interesting, but less studied
aspect is the structural component of random number generation as opposed to
the algorithmic aspect. That is, given a network structure imposed by nature or
physical devices, how can we build networks that generate arbitrary probability
distributions in an optimal way? In this paper, we study the generation of
arbitrary probability distributions in multivalued relay circuits, a
generalization in which relays can take on any of N states and the logical
'and' and 'or' are replaced with 'min' and 'max' respectively. Previous work
was done on two-state relays. We generalize these results, describing a duality
property and networks that generate arbitrary rational probability
distributions. We prove that these networks are robust to errors and design a
universal probability generator which takes input bits and outputs arbitrary
binary probability distributions