With recent reports on near-atomic-resolution (i.e., 3–4 Å) structures for several icosahedral viruses and resolutions in the range of 4–6 Å for complexes with less or no symmetry, cryo-electron microscopy (cryo-EM) single-particle analysis has entered the exciting stage where it may be used for *de novo* generation of atomic models.^{1} However, the observation that reported resolutions vary significantly for maps with otherwise similar features^{2} is an indication that existing reconstruction methods suffer from different degrees of overfitting. Overfitting occurs when the reconstruction describes noise instead of the underlying signal in the data, and often, these noisy features are enhanced during iterative refinement procedures. Thereby, overfitting is not merely an issue of comparing the resolution of one reconstruction with another but represents a major obstacle in the objective analysis of cryo-EM maps. In particular, without a useful cross-validation tool, such as the free *R*-factor in X-ray crystallography,^{3} overfitting may remain undetected and a map may be interpreted at a resolution where the features are mainly due to noise.

At the heart of the problem lies the indirectness of the experimental observations. A reasonably good model is available for the image formation process. Given a three-dimensional (3D) structure, this so-called forward model describes the appearance of the experimental images. However, the problem of single-particle reconstruction is the inverse one and is much more difficult to solve. The structure determination task is further complicated by the lack of information about the relative orientations of all particles and, in the case of structural variability in the sample, also their assignment to a structurally unique class. These data are lost during the experiment, where molecules in distinct conformations coexist in solution and adopt random orientations in the ice. In mathematics, this type of problem where part of the data is missing is called *incomplete*. Moreover, because the electron exposure of the sample needs to be strictly limited to prevent radiation damage, experimental cryo-EM images are extremely noisy. The high levels of noise together with the incompleteness of the data mean that cryo-EM structures are not fully determined by the experimental data and therefore prone to overfitting. In mathematical terms, the cryo-EM structure determination problem is *ill-posed*.

Ill-posed problems can be tackled by regularization, where the experimental data are complemented with external or prior information so that the two sources of information together fully determine a unique solution. A particularly powerful source of prior information about cryo-EM reconstructions is smoothness. Because macromolecules consist of atoms that are connected through chemical bonds, the scattering potential will vary smoothly in space, especially at less than atomic resolution. The concept of imposing smoothness to prevent overfitting is widely used in the field through a variety of *ad hoc* filtering procedures. By limiting the power of the reconstruction at those frequencies where the signal-to-noise ratio (SNR) is low, these filters impose smoothness on the reconstructed density in real space. Traditionally, filtering procedures have relied on heuristics, that is, to some extent, existing implementations are all based on arbitrary decisions. Although potentially highly effective (and this is illustrated by the high-resolution structures mentioned above), the heuristics in these methods often involve the tuning of free parameters, such as low-pass filter shape and effective resolution (e.g., see Ref. ^{4}). Thereby, the user (or, in some cases, the programmer) becomes responsible for the delicate balance between getting the most out of the data and limiting overfitting, which ultimately may lead to subjectivity in the structure determination process.

Recent attention for statistical image processing methods^{5} could be explained by a general interest in reducing the amount of heuristics in cryo-EM reconstruction procedures. Rather than combining separate steps of particle alignment, class averaging, filtering, and 3D reconstruction, each of which may involve arbitrary decisions, the statistical approach seeks to maximize a single probability function. Most of the statistical methods presented thus far have optimized a likelihood function, that is, one aims to find the model that has the highest probability of being the correct one in the light of the observed data. This has important theoretical advantages, as the maximum likelihood (ML) estimate is asymptotically unbiased and efficient. That is, in the limit of very large data sets, the ML estimate is as good as or better than any other estimate of the true model (see Ref. ^{6} for a recent review on ML methods in cryo-EM). In practice, however, data sets are not very large, and also in the statistical approach, the experimental data may need to be supplemented with prior information in order to define a unique solution. In Bayesian statistics, regularization is interpreted as imposing prior distributions on model parameters, and the ML optimization target may be augmented with such prior distributions. Optimization of the resulting posterior distribution is called *regularized likelihood* optimization, or *maximum a posteriori* (MAP) estimation (see Ref. ^{7}).

In this paper, I will show that MAP estimation provides a self-contained statistical framework in which the regularized single-particle reconstruction problem can be solved with only a minimal amount of heuristics. As a prior, I will use a Gaussian distribution on the Fourier components of the signal. Neither the use of this prior nor that of the Bayesian treatment of cryo-EM data is a new idea. Standard textbooks on statistical inference use the same prior in a Bayesian interpretation of the commonly used Wiener filter (e.g., see Ref. ^{7}, pp. 549–551), and an early mention of MAP estimation with a Gaussian prior in the context of 3D EM image restoration was given by Carazo.^{8} Nevertheless, even though these ideas have been around for many years, the Bayesian approach has thus far not found wide-spread use in 3D EM structure determination (see Ref. ^{9} for a recent application). This limited use contrasts with other methods in structural biology. Recently, Bayesian inference was shown to be highly effective in NMR structure determination,^{10} while the Bayesian approach was introduced to the field of X-ray crystallography many years ago^{11} and MAP estimation is now routinely used in crystallographic refinement.^{12}

In what follows, I will first describe some of the underlying theory of existing cryo-EM structure determination procedures to provide a context for the statistical approach. Then, I will derive an iterative MAP estimation algorithm that employs a Gaussian prior on the model in Fourier space. Because statistical assumptions about the signal and the noise are made explicit in the target function, straightforward calculus in the optimization of this target leads to valuable new insights into the optimal linear (or Wiener) filter in the context of 3D reconstruction and the definition of the 3D SNR in the Fourier transform of the reconstruction. Moreover, because the MAP algorithm requires only a minimum amount of heuristics, arbitrary decisions by the user or the programmer may be largely avoided, and objectivity may be preserved. I will demonstrate the effectiveness of the statistical approach by application to three cryo-EM data sets and compare the results with those obtained using conventional methods. Apart from overall improvements in the reconstructed maps and the ability to detect smaller classes in structurally heterogeneous data sets, the statistical approach reduces overfitting and provides reconstructions with more reliable resolution estimates.