This technical report treats some technical considerations related to the probability density function of a function of a random vector.

structural repetitive subsequences are most important portion of biological sequences, which play crucial roles on corresponding sequence’s fold and functionality. biggest class of the repetitive subsequences is “transposable elements” which has its own sub-classes upon contexts’ structures. many researches have been performed to criticality determine the structure and function of repetitive su...

Merging the field of epidemiology with those of evolutionary and behavioural ecology can generate considerable fundamental knowledge, as well as help to guide public health policies. An attempt is made here to integrate these disciplines by focusing on parasitic manipulation in vector-borne diseases. Parasitic manipulation is a fascinating strategy of transmission which occurs when a parasite a...

A 0-1 probability space is a probability space ((; 2 ; P), where the sample space f0;1g n for some n. A probability space is k-wise independent if, when Yi is deened to be the ith coordinate of the random n-vector, then any subset of k of the Yi's is (mutually) independent, and it is said to be a probability space for p1; p2; :::;pn if PYi = 1] = pi. We study constructions of k-wise independent...

Chagas disease has a major impact on human health in Latin America and is becoming of global concern due to international migrations. Trypanosoma cruzi, the etiological agent of the disease, is one of the rare human parasites transmitted by the feces of its vector, as it is unable to reach the salivary gland of the insect. This stercorarian transmission is notoriously poorly understood, despite...

The analysis of discrimination, feature and model selection conduct to the discussion of the relationships between Support Vector Machine (SVM), Bayesian and Maximum Entropy (MaxEnt) formalisms. MaxEnt discrimination can be seen as a particular case of Bayesian inference, which at its turn can be seen as a regularization approach applicable to SVM. Probability measures can be attached to each f...

In this paper, we analyze the performance of dither modulation (DM) against the composite attacks including valumetric scaling, additive noise and constant change. The analyses are developed under the assumptions that the host vector and noise vector are mutually independent and both of them have independently and identically distributed components. We derive the general expressions of the prob...

An unsupervised learning algorithm for a multilayer network of stochastic neurons is described. Bottom-up "recognition" connections convert the input into representations in successive hidden layers, and top-down "generative" connections reconstruct the representation in one layer from the representation in the layer above. In the "wake" phase, neurons are driven by recognition connections, and...

A state i is said to be transient if fii < 1; and it is said to be persistent if fii = 1. Denote by q = (q 1, q t 2, . . . , q t n) the state probability vector (the distribution of the chain at time t), to be a row vector whose i-th component is the probability that the Markov chain is in state i at time t. A distribution π is a stationary distribution for a Markov chain with transition matrix...

In this paper, a new support vector machine classifier with probabilistic constrains is proposed which presence probability of samples in each class is determined based on a distribution function. Noise is caused incorrect calculation of support vectors thereupon margin can not be maximized. In the proposed method, constraints boundaries and constraints occurrence have probability density funct...