We propose a non-linear graphical model for structured prediction. It combines the power of deep neural networks to extract high level features with the graphical framework of Markov networks, yielding a powerful and scalable probabilistic model that we apply to signal labeling tasks.

1 Hidden Markov Model 1.1 General parametric form In hidden Markov model (HMM), we have three sets of parameters, transition probability matrix A : p(y t = 1|y t−1 = 1) = ai,j , initialprobabilities : p(y1) ∼ Multinomial(π1, π2, ..., πM ), emission probabilities : p(xt|y t) ∼ Multinomial(bi,1, bi,2, ..., bi,K). 1.2 Inference The inference can be done with forward algorithm which computes α t ≡ ...

We propose Bayesian Conditional Random Fields (BCRFs) for classifying interdependent and structured data, such as sequences, images or webs. BCRFs are a Bayesian approach to training and inference with conditional random fields, which were previously trained by maximizing likelihood (ML) (Lafferty et al., 2001). Our framework eliminates the problem of overfitting, and offers the full advantages...

In this paper we study the asymptotic behavior for the tail probability of the randomly weighted sums and their maximum where the usual assumption of independence of random variable X and indepedence between the variable X and the weighted θ are relaxed. We suppose that, the variable X follows a conditional dependence intoduced by Geluk and Tang [6] and the pair (X, θ) follows a certain depende...

confirmation of convergence hypothesis among provinces, its role in removing divergence among regions and achieving regional convergence is one of the important issues in macroeconomics. the aim of the present study is to examine absolute and conditional income convergence hypotheses among iranian provinces during the period 2000-2010 using panel data. the hypotheses are tested for the whole pr...

An evergreen debate in Finance concerns the rules for making portfolio hedge decisions. A traditional tool proposed in the literature is the well-known standard deviation based Sharpe Ratio, which has been recently generalized in order to involve also other popular risk measures ρ, such as VaR (Value-at-Risk) or CVaR (Conditional Value at Risk). This approach gives the correct choice of portfol...

Exploiting the coherent medium approximation, I investigate a random walk on objects distributed randomly in continuous space when jump rate depends distance between two adjacent objects. In one dimension, it is shown that decays exponentially long limit, non-diffusive to diffusive transition occurs as density of sites increased. three dimensions, exists has super Gaussian decay.

We consider a critical nearest neighbor branching random walk on the d−dimensional integer lattice. Denote by Vm the maximal number of particles at a single site at time m, and by Gm the event that the branching random walk survives to generation m. We show that if the offspring distribution has finite n-th moment, then in dimensions d ≥ 3, conditional on Gm, Vm = Op(m 1 n ); and if the offspri...

Conditional distribution reflects the dependency link among random variables, but two-dimensional random variables Conditional Distribution has some limitations. In order to rich the content of conditional distribution this paper gives the extension of conditional distribution and examples in the case of continuous random variables. For the given definition of conditional distribution of three-...