Abstract:
After making some general remarks, I consider two examples that illustrate the use of Bayesian Probability Theory. The first is a simple one, the physicist's favorite "toy," that provides a forum for a discussion of the key conceptual issue of Bayesian analysis: the assignment of prior probabilities. The other example illustrates the use of Bayesian ideas in the real world of experimental physics.

Abstract:
These lectures introduce key concepts in probability and statistical inference at a level suitable for graduate students in particle physics. Our goal is to paint as vivid a picture as possible of the concepts covered.

Abstract:
We introduce a few of the key ideas of statistical analysis using two real-world examples to illustrate how these ideas are used in practice.

Abstract:
The observation that Type Ia supernovae are fainter than expected given their red shifts has led to the conclusion that the expansion of the universe is accelerating. The widely accepted hypothesis is that this acceleration is caused by a cosmological constant or, more generally, some dark energy field that pervades the universe. This hypothesis presents a challenge to physics so severe that one is motivated to explore alternative explanations. In this paper, we explore whether the data from Type Ia supernovae can be explained with an idea that is almost as old as that of the cosmological constant, namely, that the strength of gravity varies on a cosmic timescale. This topic is an ideal one for investigation by an undergraduate physics major because the entire chain of reasoning from models to data analysis is well within the mathematical and conceptual sophistication of a motivated undergraduate.

Abstract:
Bayesian inferences in high energy physics often use uniform prior distributions for parameters about which little or no information is available before data are collected. The resulting posterior distributions are therefore sensitive to the choice of parametrization for the problem and may even be improper if this choice is not carefully considered. Here we describe an extensively tested methodology, known as reference analysis, which allows one to construct parametrization-invariant priors that embody the notion of minimal informativeness in a mathematically well-defined sense. We apply this methodology to general cross section measurements and show that it yields sensible results. A recent measurement of the single top quark cross section illustrates the relevant techniques in a realistic situation.

Abstract:
We examine the prospects for probing heavy top quark-antiquark (t-tbar) resonances at the upgraded LHC in pp collisions at $\root_s = 14 TeV. Heavy t-tbar resonances (Z' bosons) are predicted by several theories that go beyond the standard model. We consider scenarios in which each top quark decays leptonically, either to an electron or a muon, and the data sets correspond to integrated luminosities of \int L dt = 300 /fb and \int L dt = 3000 /fb. We present the expected 5-sigma discovery potential for a Z' resonance as well as the expected upper limits at 95% C.L. on the Z' production cross section and mass in the absence of a discovery.

Abstract:
We have studied the potential of the CDF and DZero experiments to discover a low-mass Standard Model Higgs boson, during Run II, via the processes $p\bar{p}$ -> WH -> $\ell\nu b\bar{b}$, $p\bar{p}$ -> ZH -> $\ell^{+}\ell^{-}b\bar{b}$ and $p\bar{p}$ -> ZH ->$\nu \bar{\nu} b\bar{b}$. We show that a multivariate analysis using neural networks, that exploits all the information contained within a set of event variables, leads to a significant reduction, with respect to {\em any} equivalent conventional analysis, in the integrated luminosity required to find a Standard Model Higgs boson in the mass range 90 GeV/c**2 < M_H < 130 GeV/c**2. The luminosity reduction is sufficient to bring the discovery of the Higgs boson within reach of the Tevatron experiments, given the anticipated integrated luminosities of Run II, whose scope has recently been expanded.

Abstract:
The interpretation of data in terms of multi-parameter models of new physics, using the Bayesian approach, requires the construction of multi-parameter priors. We propose a construction that uses elements of Bayesian reference analysis. Our idea is to initiate the chain of inference with the reference prior for a likelihood function that depends on a single parameter of interest that is a function of the parameters of the physics model. The reference posterior density of the parameter of interest induces on the parameter space of the physics model a class of posterior densities. We propose to continue the chain of inference with a particular density from this class, namely, the one for which indistinguishable models are equiprobable and use it as the prior for subsequent analysis. We illustrate our method by applying it to the constrained minimal supersymmetric Standard Model and two non-universal variants of it.