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In this paper we introduce ZhuSuan, a python probabilistic programming library for Bayesian deep learning, which conjoins the complimentary advantages of Bayesian methods and deep learning. ZhuSuan is built upon Tensorflow. Unlike existing deep learning libraries, which are mainly designed for deterministic neural networks and supervised tasks, ZhuSuan is featured for its deep root into Bayesia...

Recursive non-linear Bayesian estimation is addressed using equivalence approach as motivating framework. Its specific form – tailored to a model class covering non-normal ARX (auto-regression with exogenous variables) models, models with discrete outputs and continuous-valued regression vectors and their dynamic mixtures – is presented. The resulting algorithms provide efficient solutions of d...

Bayesian methods furnish an attractive approach to time series data analysis. This article proposes the forecasting models that can detect trend, seasonality, auto regression and outliers in time series data related to some covariates. Cumulative Weibull distribution functions for trend, dummy variables for seasonality, binary selections for outliers and latent autoregression for autocorrelated...

This paper studies how to combine real-time forecasts from a broad range of Bayesian vector autoregression (BVAR) specifications and survey by optimally exploiting their properties. To do that, it compares the forecasting performance optimal pooling tilting techniques, including for predicting euro area inflation GDP growth at medium-term forecast horizons using both univariate multivariate met...

This study analyses the impact of fiscal policy on South African economy during period 1972Q1-2020Q2. The adopted quarterly time series data to estimate a Bayesian Vector Autoregression (BVAR) model with selection hierarchical priors. variables employed for empirical investigation included GDP, government expenditure, public debt, and gross fixed-capital formation. results show that an unexpect...