Value investing via Bayesian inference

Value Elimination: Bayesian Inference via Backtracking Search

We present Value Elimination, a new algorithm for Bayesian Inference. Given the same variable order­ ing information, Value Elimination can achieve per­ formance that is within a constant factor of variable elimination or recursive conditioning, and on some problems it can perform exponentially better, irrespec­ tive of the variable ordering used by these algorithms. Value Elimination's other f...

Bayesian inference via projections

Bayesian inference often poses difficult computational problems. Even when off-the-shelf Markov chain Monte Carlo (MCMC) methods are available to the problem at hand, mixing issues might compromise the quality of the results. We introduce a framework for situations where the model space can be naturally divided into two components: i. a baseline black-box probability distribution for the observ...

Bayesian inference via rejection filtering

We provide a method for approximating Bayesian inference using rejection sampling. We not only make the process efficient, but also dramatically reduce the memory required relative to conventional methods by combining rejection sampling with particle filtering. We also provide an approximate form of rejection sampling that makes rejection filtering tractable in cases where exact rejection sampl...

Extreme Value GARCH modelling with Bayesian Inference

Extreme value theory is widely used financial applications such as risk analysis, forecasting and pricing models. One of the major difficulties in the applications to finance and economics is that the assumption of independence of time series observations is generally not satisfied, so that the dependent extremes may not necessarily be in the domain of attraction of the classical generalised ex...

Bayesian inference: statistical gimmick or added value?