Related papers: Estimating meteor rates using Bayesian inference
A test of a new Bayesian approach to solar flare prediction (Wheatland 2004a) is presented. The approach uses the past history of flaring together with phenomenological rules of flare statistics to make a prediction for the probability of…
In Generalised Bayesian Inference (GBI), the learning rate and hyperparameters of the loss must be estimated. These inference-hyperparameters can't be estimated jointly with the other parameters, from the data, by giving them a prior.…
We demonstrate that the principle of maximum relative entropy (ME), used judiciously, can ease the specification of priors in model selection problems. The resulting effect is that models that make sharp predictions are disfavoured,…
In meteor physics the luminous efficiency ${\tau}$ is used to convert the meteor's magnitude to the corresponding meteoroid's mass. However, lack of sufficiently accurate verification methods or adequate laboratory tests leave this…
A common problem in disciplines of applied Statistics research such as Astrostatistics is of estimating the posterior distribution of relevant parameters. Typically, the likelihoods for such models are computed via expensive experiments…
Meteoroids entering the Earth's atmosphere can be observed as meteors, thereby providing useful information on their formation and hence on their parent bodies. We developed a data reduction software package for double station meteor data…
In Bayesian theory, calculating a posterior probability distribution is highly important but usually difficult. Therefore, some methods have been put forward to deal with such problem, among which, the most popular one is the asymptotic…
To better estimate which luminous efficiency ($\tau$) value is compatible with contemporary values of the ionization coefficient ($\beta$), we report a series of simultaneous optical and specular echo radar measurements of low speed…
Given a set of moment restrictions (MRs) that overidentify a parameter $\theta$, we investigate a semiparametric Bayesian approach for inference on $\theta$ that does not restrict the data distribution $F$ apart from the MRs. As main…
In this paper we consider the estimation of unknown parameters in Bayesian inverse problems. In most cases of practical interest, there are several barriers to performing such estimation, This includes a numerical approximation of a…
Despite the progress within the last decades, weather forecasting is still a challenging and computationally expensive task. Current satellite-based approaches to predict thunderstorms are usually based on the analysis of the observed…
This report introduces general ideas and some basic methods of the Bayesian probability theory applied to physics measurements. Our aim is to make the reader familiar, through examples rather than rigorous formalism, with concepts such as:…
We consider the problem of parametric statistical inference when likelihood computations are prohibitively expensive but sampling from the model is possible. Several so-called likelihood-free methods have been developed to perform inference…
The problem of assigning probability distributions which objectively reflect the prior information available about experiments is one of the major stumbling blocks in the use of Bayesian methods of data analysis. In this paper the method of…
There are several assumptions made in a standard $\chi^2$ analysis of data, including the frequent assumption that the likelihood function is well approximated by a multivariate Gaussian distribution. This article briefly reviews the…
We use Bayesian analysis in order to constrain the equation of state for nuclear matter from astrophysical data related to the recent measurements from the NICER mission, LIGO/Virgo collaboration, and probability distributions of mass and…
This article presents a new method for estimating the amount of an artifact class in use at a given moment in the past from a random assemblage of archaeological finds. This method is based on the use of simulation, since an analytical…
This paper proposes iterative Bayesian method to estimate success probability based on unique sample. The procedure is replacing the distribution characteristic of prior with Bayes estimate on the every iteration until they coincide.…
We have investigated the systematic differences introduced when performing a Bayesian-inference analysis of the equation of state of neutron stars employing either variable- or constant-likelihood functions. The former have the advantage…
Performing exact inference on Bayesian networks is known to be #P-hard. Typically approximate inference techniques are used instead to sample from the distribution on query variables given the values $e$ of evidence variables. Classically,…