Time Series Models

This chapter reviews some important ideas from time series analysis. The concepts of stationarity, independence, and exchangeability are defined and illustrated with examples. The Poisson process is examined in detail and then the class of linear processes, noting the implications of the Wold decomposition. The final section studies the random walk and the reflection principle.

Stochastic Processes in Continuous Time

This chapter reviews the theory of continuous-time stochastic processes, covering the concepts of adaptation, Lévy processes, diffusions, martingales, and Markov processes. Brownian motion is studied as the most important case, with properties that include the reflection principle and the strong Markov property. The technique of Skorokhod embedding is introduced, providing novel proofs of the central limit theorem and the law of the iterated logarithm. The family of processes derived from Brownian motion is reviewed and in the final section it is shown that a continuous process having finite variance and independent increments is Brownian motion.

Canonical fragments of the strong reflection principle

For an arbitrary forcing class [Formula: see text], the [Formula: see text]-fragment of Todorčević’s strong reflection principle SRP is isolated in such a way that (1) the forcing axiom for [Formula: see text] implies the [Formula: see text]-fragment of SRP , (2) the stationary set preserving fragment of SRP is the full principle SRP , and (3) the subcomplete fragment of SRP implies the major consequences of the subcomplete forcing axiom. This fragment of SRP is consistent with CH , and even with Jensen’s principle [Formula: see text]. Along the way, some hitherto unknown effects of (the subcomplete fragment of) SRP on mutual stationarity are explored, and some limitations to the extent to which fragments of SRP may capture the effects of their corresponding forcing axioms are established.

Bayesian Updating When What You Learn Might Be False (Forthcoming in Erkenntnis)

Rescorla (Erkenntnis, 2020) has recently pointed out that the standard arguments for Bayesian Conditionalization assume that whenever I become certain of something, it is true. Most people would reject this assumption. In response, Rescorla offers an improved Dutch Book argument for Bayesian Conditionalization that does not make this assumption. My purpose in this paper is two-fold. First, I want to illuminate Rescorla’s new argument by giving a very general Dutch Book argument that applies to many cases of updating beyond those covered by Conditionalization, and then showing how Rescorla’s version follows as a special case of that. Second, I want to show how to generalise R. A. Briggs and Richard Pettigrew’s Accuracy Dominance argument to avoid the assumption that Rescorla has identified (Briggs and Pettigrew in Noûs, 2018). In both cases, these arguments proceed by first establishing a very general reflection principle.

One Absolutely Infinite Universe to Rule Them All

We contrast two Universe-outlooks and universality-sources. The first—localism—runs bottom-up and is in the vein of modern iterative set theory, generating ever more sets but all limited unities and barring an ur-Universe taken as a primary—the prime-object/unity. This contrasts with an absolutely infinite Universe-first outlook, globalism, inspired by some remarks on Cantor but later exiled by Zermelo. The metaphysics is now all top-down, and all sets (e.g. large cardinals) are regarded as generated fragments. The role of the (Godel-central) reflection principle is dissected.?